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Alathari MJA, Mashhadany YA, Bakar AAA, Mokhtar MHH, Bin Zan MSD, Arsad N. COVID-19 IgG antibodies detection based on CNN-BiLSTM algorithm combined with fiber-optic dataset. J Virol Methods 2024; 330:115011. [PMID: 39154936 DOI: 10.1016/j.jviromet.2024.115011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2024] [Revised: 07/14/2024] [Accepted: 08/15/2024] [Indexed: 08/20/2024]
Abstract
The urgent need for efficient and accurate automated screening tools for COVID-19 detection has led to research efforts exploring various approaches. In this study, we present pioneering research on COVID-19 detection using a hybrid model that combines convolutional neural networks (CNN) with a bi-directional long short-term memory (Bi-LSTM) network, in conjunction with fiber optic data for SARS-CoV-2 Immunoglobulin G (IgG) antibodies. Our research introduces a comprehensive data preprocessing pipeline and evaluates the performance of four different deep learning (DL) algorithms: CNN, CNN-RNN, BiLSTM, and CNN-BiLSTM, in classifying samples as positive or negative for the COVID-19 virus. Among these, the CNN-BiLSTM classifier demonstrated superior performance on the training datasets, achieving an accuracy of 89 %, a recall of 88 %, a precision of 90 %, an F1-score of 89 %, a specificity of 90 %, a geometric mean (G-mean) of 89 %, and a receiver operating characteristic (ROC) of 96 %. In addition, the achieved classification results were compared with those reported in the literature. The findings indicate that the proposed model has promising potential for classifying COVID-19 and could serve as a valuable tool for healthcare professionals. The use of IgG antibodies to detect the virus enhances the specificity and accuracy of the diagnostic tool.
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Affiliation(s)
- Mohammed Jawad Ahmed Alathari
- UKM - Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM, Bangi 43600, Malaysia.
| | - Yousif Al Mashhadany
- Department of Electrical Engineering, College of Engineering, Anbar University, Anbar 00964, Iraq.
| | - Ahmad Ashrif A Bakar
- UKM - Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM, Bangi 43600, Malaysia.
| | - Mohd Hadri Hafiz Mokhtar
- UKM - Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM, Bangi 43600, Malaysia.
| | - Mohd Saiful Dzulkefly Bin Zan
- UKM - Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM, Bangi 43600, Malaysia.
| | - Norhana Arsad
- UKM - Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM, Bangi 43600, Malaysia.
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2
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Sun P, Liu H, Zhao Y, Hao N, Deng Z, Zhao W. Construction of an antidepressant priority list based on functional, environmental, and health risks using an interpretable mixup-transformer deep learning model. JOURNAL OF HAZARDOUS MATERIALS 2024; 474:134651. [PMID: 38843640 DOI: 10.1016/j.jhazmat.2024.134651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 05/15/2024] [Accepted: 05/17/2024] [Indexed: 06/26/2024]
Abstract
As emerging pollutants, antidepressants (AD) must be urgently investigated for risk identification and assessment. This study constructed a comprehensive-effect risk-priority screening system (ADRank) for ADs by characterizing AD functionality, occurrence, persistence, bioaccumulation and toxicity based on the integrated assignment method. A classification model for ADs was constructed using an improved mixup-transformer deep learning method, and its classification accuracy was compared with those of other models. The accuracy of the proposed model improved by up to 23.25 % compared with the random forest model, and the reliability was 80 % more than that of the TOPSIS method. A priority screening candidate list was proposed to screen 33 high-priority ADs. Finally, SHapley Additive explanation (SHAP) visualization, molecular dynamics, and amino acid analysis were performed to analyze the correlation between AD structure and toxic receptor binding characteristics and reveal the differences in AD risk priority. ADs with more intramolecular hydrogen bonds, higher hydrophobicity, and electronegativity had a more significant risk. Van der Waals and electrostatic interactions were the primary influencing factors, and significant differences in the types and proportions of the main amino acids in the interaction between ADs and receptors were observed. The results of the study provide constructive schemes and insights for AD priority screening and risk management.
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Affiliation(s)
- Peixuan Sun
- College of New Energy and Environment, Jilin University, Changchun 130012, China
| | - Huaishi Liu
- College of Instrumentation and Electrical Engineering, Jilin University, Changchun 130000, China
| | - Yuanyuan Zhao
- College of New Energy and Environment, Jilin University, Changchun 130012, China
| | - Ning Hao
- College of New Energy and Environment, Jilin University, Changchun 130012, China
| | - Zhengyang Deng
- College of New Energy and Environment, Jilin University, Changchun 130012, China
| | - Wenjin Zhao
- College of New Energy and Environment, Jilin University, Changchun 130012, China.
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3
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Negi S, Mathur A, Tripathy S, Mehta V, Snigdha NT, Adil AH, Karobari MI. Artificial Intelligence in Dental Caries Diagnosis and Detection: An Umbrella Review. Clin Exp Dent Res 2024; 10:e70004. [PMID: 39206581 PMCID: PMC11358700 DOI: 10.1002/cre2.70004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 04/29/2024] [Accepted: 08/16/2024] [Indexed: 09/04/2024] Open
Abstract
BACKGROUND AND AIM Dental caries is largely preventable, yet an important global health issue. Numerous systematic reviews have summarized the efficacy of artificial intelligence (AI) models for the diagnosis and detection of dental caries. Therefore, this umbrella review aimed to synthesize the results of systematic reviews on the application and effectiveness of AI models in diagnosing and detecting dental caries. METHODS MEDLINE/PubMed, IEEE Explore, Embase, and Cochrane Database of Systematic Reviews were searched to retrieve studies. Two authors independently screened the articles based on eligibility criteria and then, appraised the included articles. The findings are summarized in tabulation form and discussed using the narrative method. RESULT A total of 1249 entries were identified out of which 7 were finally included. The most often employed AI algorithms were the multilayer perceptron, support vector machine (SVM), and neural networks. The algorithms were built to perform the segmentation, classification, caries detection, diagnosis, and caries prediction from several sources, including periapical radiographs, panoramic radiographs, smartphone images, bitewing radiographs, near-infrared light transillumination images, and so forth. Convoluted neural networks (CNN) demonstrated high sensitivity, specificity, and area under the curve in the caries detection, segmentation, and classification tests. Notably, AI in conjunction with periapical and panoramic radiography images yielded better accuracy in detecting and diagnosing dental caries. CONCLUSION AI models, especially convolutional neural network (CNN)-based models, have an enormous amount of potential for accurate, objective dental caries diagnosis and detection. However, ethical considerations and cautious adoption remain critical to its successful integration into routine practice.
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Affiliation(s)
- Sapna Negi
- Department of Dental Research Cell, Dr. D. Y. Patil Dental College and HospitalDr. D. Y. Patil VidyapeethPuneMaharashtraIndia
| | - Ankita Mathur
- Department of Dental Research Cell, Dr. D. Y. Patil Dental College and HospitalDr. D. Y. Patil VidyapeethPuneMaharashtraIndia
| | - Snehasish Tripathy
- Department of Dental Research Cell, Dr. D. Y. Patil Dental College and HospitalDr. D. Y. Patil VidyapeethPuneMaharashtraIndia
| | - Vini Mehta
- Department of Dental Research Cell, Dr. D. Y. Patil Dental College and HospitalDr. D. Y. Patil VidyapeethPuneMaharashtraIndia
| | - Niher Tabassum Snigdha
- Department of Dental Research, Saveetha Medical College and Hospitals, Saveetha Institute of Medical and Technical SciencesSaveetha UniversityChennaiTamil NaduIndia
| | - Abdul Habeeb Adil
- Department of Dental Research, Saveetha Medical College and Hospitals, Saveetha Institute of Medical and Technical SciencesSaveetha UniversityChennaiTamil NaduIndia
| | - Mohmed Isaqali Karobari
- Department of Dental Research, Saveetha Medical College and Hospitals, Saveetha Institute of Medical and Technical SciencesSaveetha UniversityChennaiTamil NaduIndia
- Department of Restorative Dentistry & Endodontics, Faculty of DentistryUniversity of PuthisastraPhnom PenhCambodia
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4
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Zhang C, Tian H, Zhang L, Jiao P. Robust AUC optimization under the supervision of clean data. Sci Rep 2024; 14:16693. [PMID: 39030210 PMCID: PMC11271567 DOI: 10.1038/s41598-024-66788-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Accepted: 07/03/2024] [Indexed: 07/21/2024] Open
Abstract
AUC (area under the ROC curve) is an essential metric that has been extensively researched in the field of machine learning. Traditional AUC optimization methods need a large-scale clean dataset, while real-world datasets usually contain massive noisy samples. To reduce the impact of noisy samples, many robust AUC optimization methods have been proposed. However, these methods only use noisy data and ignore the effect of clean data. To make full use of clean data and noisy data, in this paper, we propose a new framework for AUC optimization which uses clean samples to guide the processing of the noisy dataset based on the technology of self-paced learning (SPL). Innovatively, we introduce the consistency regularization term to reduce the negative impact of the data enhancement technology on SPL. Traditional SPL methods usually suffer from the high complexity of alternately solving the two critical sub-problems with respect to sample weights and model parameters. To speed up the training process, we propose a new efficient algorithm to solve our problem, which alternately updates sample weights and model parameters with the stochastic gradient method. Theoretically, we prove that our new optimization method can converge to a stationary point. Comprehensive experiments demonstrate that our robust AUC optimization (RAUCO) algorithm holds better robustness than existing algorithms.
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Affiliation(s)
- Chenkang Zhang
- China Mobile (Suzhou) Software Technology Company Limited, Suzhou, 215163, China.
| | - Haobing Tian
- China Mobile (Suzhou) Software Technology Company Limited, Suzhou, 215163, China
| | - Lang Zhang
- China Mobile (Suzhou) Software Technology Company Limited, Suzhou, 215163, China
| | - Pengju Jiao
- China Mobile (Suzhou) Software Technology Company Limited, Suzhou, 215163, China
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Chharia A, Jeevan G, Jha RA, Liu M, Berman JM, Glorioso C. Accuracy of US CDC COVID-19 forecasting models. Front Public Health 2024; 12:1359368. [PMID: 38989122 PMCID: PMC11233691 DOI: 10.3389/fpubh.2024.1359368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 06/07/2024] [Indexed: 07/12/2024] Open
Abstract
Accurate predictive modeling of pandemics is essential for optimally distributing biomedical resources and setting policy. Dozens of case prediction models have been proposed but their accuracy over time and by model type remains unclear. In this study, we systematically analyze all US CDC COVID-19 forecasting models, by first categorizing them and then calculating their mean absolute percent error, both wave-wise and on the complete timeline. We compare their estimates to government-reported case numbers, one another, as well as two baseline models wherein case counts remain static or follow a simple linear trend. The comparison reveals that around two-thirds of models fail to outperform a simple static case baseline and one-third fail to outperform a simple linear trend forecast. A wave-by-wave comparison of models revealed that no overall modeling approach was superior to others, including ensemble models and errors in modeling have increased over time during the pandemic. This study raises concerns about hosting these models on official public platforms of health organizations including the US CDC which risks giving them an official imprimatur and when utilized to formulate policy. By offering a universal evaluation method for pandemic forecasting models, we expect this study to serve as the starting point for the development of more accurate models.
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Affiliation(s)
- Aviral Chharia
- Global Health Research Collective, Academics for the Future of Science, Cambridge, MA, United States
- Data Informatics Center for Epidemiology, PathCheck Foundation, Cambridge, MA, United States
- Department of Mechanical Engineering, Thapar Institute of Engineering and Technology, Patiala, PB, India
| | - Govind Jeevan
- Global Health Research Collective, Academics for the Future of Science, Cambridge, MA, United States
- Data Informatics Center for Epidemiology, PathCheck Foundation, Cambridge, MA, United States
| | - Rajat Aayush Jha
- Global Health Research Collective, Academics for the Future of Science, Cambridge, MA, United States
- Data Informatics Center for Epidemiology, PathCheck Foundation, Cambridge, MA, United States
| | - Meng Liu
- Global Health Research Collective, Academics for the Future of Science, Cambridge, MA, United States
- Department of Industrial and Manufacturing Engineering, Penn State University, University Park, PA, United States
| | - Jonathan M Berman
- Global Health Research Collective, Academics for the Future of Science, Cambridge, MA, United States
- Department of Basic Science, New York Institute of Technology, College of Osteopathic Medicine at Arkansas State University, Jonesboro, AR, United States
| | - Christin Glorioso
- Global Health Research Collective, Academics for the Future of Science, Cambridge, MA, United States
- Data Informatics Center for Epidemiology, PathCheck Foundation, Cambridge, MA, United States
- Department of Anatomy, University of California, San Francisco, San Francisco, CA, United States
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Burton J, Farrell S, Mäntylä Noble PJ, Al Moubayed N. Explainable text-tabular models for predicting mortality risk in companion animals. Sci Rep 2024; 14:14217. [PMID: 38902282 PMCID: PMC11190214 DOI: 10.1038/s41598-024-64551-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 06/10/2024] [Indexed: 06/22/2024] Open
Abstract
As interest in using machine learning models to support clinical decision-making increases, explainability is an unequivocal priority for clinicians, researchers and regulators to comprehend and trust their results. With many clinical datasets containing a range of modalities, from the free-text of clinician notes to structured tabular data entries, there is a need for frameworks capable of providing comprehensive explanation values across diverse modalities. Here, we present a multimodal masking framework to extend the reach of SHapley Additive exPlanations (SHAP) to text and tabular datasets to identify risk factors for companion animal mortality in first-opinion veterinary electronic health records (EHRs) from across the United Kingdom. The framework is designed to treat each modality consistently, ensuring uniform and consistent treatment of features and thereby fostering predictability in unimodal and multimodal contexts. We present five multimodality approaches, with the best-performing method utilising PetBERT, a language model pre-trained on a veterinary dataset. Utilising our framework, we shed light for the first time on the reasons each model makes its decision and identify the inclination of PetBERT towards a more pronounced engagement with free-text narratives compared to BERT-base's predominant emphasis on tabular data. The investigation also explores the important features on a more granular level, identifying distinct words and phrases that substantially influenced an animal's life status prediction. PetBERT showcased a heightened ability to grasp phrases associated with veterinary clinical nomenclature, signalling the productivity of additional pre-training of language models.
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Affiliation(s)
- James Burton
- Department of Computer Science, Durham University, Durham, UK.
| | - Sean Farrell
- Department of Computer Science, Durham University, Durham, UK
| | - Peter-John Mäntylä Noble
- Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, UK
| | - Noura Al Moubayed
- Department of Computer Science, Durham University, Durham, UK
- Evergreen Life Ltd, Manchester, UK
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7
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Alwhbi IA, Zou CC, Alharbi RN. Encrypted Network Traffic Analysis and Classification Utilizing Machine Learning. SENSORS (BASEL, SWITZERLAND) 2024; 24:3509. [PMID: 38894300 PMCID: PMC11175201 DOI: 10.3390/s24113509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Revised: 05/19/2024] [Accepted: 05/22/2024] [Indexed: 06/21/2024]
Abstract
Encryption is a fundamental security measure to safeguard data during transmission to ensure confidentiality while at the same time posing a great challenge for traditional packet and traffic inspection. In response to the proliferation of diverse network traffic patterns from Internet-of-Things devices, websites, and mobile applications, understanding and classifying encrypted traffic are crucial for network administrators, cybersecurity professionals, and policy enforcement entities. This paper presents a comprehensive survey of recent advancements in machine-learning-driven encrypted traffic analysis and classification. The primary goals of our survey are two-fold: First, we present the overall procedure and provide a detailed explanation of utilizing machine learning in analyzing and classifying encrypted network traffic. Second, we review state-of-the-art techniques and methodologies in traffic analysis. Our aim is to provide insights into current practices and future directions in encrypted traffic analysis and classification, especially machine-learning-based analysis.
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Affiliation(s)
| | - Cliff C. Zou
- Department of Computer Science, University of Central Florida, Orlando, FL 32816, USA; (I.A.A.); (R.N.A.)
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Abdallah AM, Doudin A, Sulaiman TO, Jamil O, Arif R, Sada FA, Yassine HM, Elrayess MA, Elzouki AN, Emara MM, Thillaiappan NB, Cyprian FS. Metabolic predictors of COVID-19 mortality and severity: a survival analysis. Front Immunol 2024; 15:1353903. [PMID: 38799469 PMCID: PMC11127595 DOI: 10.3389/fimmu.2024.1353903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Accepted: 04/15/2024] [Indexed: 05/29/2024] Open
Abstract
Introduction The global healthcare burden of COVID-19 pandemic has been unprecedented with a high mortality. Metabolomics, a powerful technique, has been increasingly utilized to study the host response to infections and to understand the progression of multi-system disorders such as COVID-19. Analysis of the host metabolites in response to SARS-CoV-2 infection can provide a snapshot of the endogenous metabolic landscape of the host and its role in shaping the interaction with SARS-CoV-2. Disease severity and consequently the clinical outcomes may be associated with a metabolic imbalance related to amino acids, lipids, and energy-generating pathways. Hence, the host metabolome can help predict potential clinical risks and outcomes. Methods In this prospective study, using a targeted metabolomics approach, we studied the metabolic signature in 154 COVID-19 patients (males=138, age range 48-69 yrs) and related it to disease severity and mortality. Blood plasma concentrations of metabolites were quantified through LC-MS using MxP Quant 500 kit, which has a coverage of 630 metabolites from 26 biochemical classes including distinct classes of lipids and small organic molecules. We then employed Kaplan-Meier survival analysis to investigate the correlation between various metabolic markers, disease severity and patient outcomes. Results A comparison of survival outcomes between individuals with high levels of various metabolites (amino acids, tryptophan, kynurenine, serotonin, creatine, SDMA, ADMA, 1-MH and carnitine palmitoyltransferase 1 and 2 enzymes) and those with low levels revealed statistically significant differences in survival outcomes. We further used four key metabolic markers (tryptophan, kynurenine, asymmetric dimethylarginine, and 1-Methylhistidine) to develop a COVID-19 mortality risk model through the application of multiple machine-learning methods. Conclusions Metabolomics analysis revealed distinct metabolic signatures among different severity groups, reflecting discernible alterations in amino acid levels and perturbations in tryptophan metabolism. Notably, critical patients exhibited higher levels of short chain acylcarnitines, concomitant with higher concentrations of SDMA, ADMA, and 1-MH in severe cases and non-survivors. Conversely, levels of 3-methylhistidine were lower in this context.
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Affiliation(s)
| | - Asmma Doudin
- Biomedical Research Center (BRC), Qatar University, Doha, Qatar
| | - Theeb Osama Sulaiman
- Department of Medicine, Hamad General Hospital, Hamad Medical Corporation, Doha, Qatar
| | - Omar Jamil
- Department of Radiology, Hamad General Hospital, Hamad Medical Corporation, Doha, Qatar
| | - Rida Arif
- Emergency Medicine Department, Hamad General Hospital, Hamad Medical Corporation, Doha, Qatar
| | - Fatima Al Sada
- Neurosurgery Department, Hamad General Hospital, Hamad Medical Corporation, Doha, Qatar
| | - Hadi M. Yassine
- Biomedical Research Center (BRC), Qatar University, Doha, Qatar
| | - Mohamed A. Elrayess
- College of Medicine, Qatar University (QU) Health, Qatar University, Doha, Qatar
- Biomedical Research Center (BRC), Qatar University, Doha, Qatar
| | - Abdel-Naser Elzouki
- College of Medicine, Qatar University (QU) Health, Qatar University, Doha, Qatar
- Department of Medicine, Hamad General Hospital, Hamad Medical Corporation, Doha, Qatar
| | - Mohamed M. Emara
- College of Medicine, Qatar University (QU) Health, Qatar University, Doha, Qatar
| | | | - Farhan S. Cyprian
- College of Medicine, Qatar University (QU) Health, Qatar University, Doha, Qatar
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Price BS, Khodaverdi M, Hendricks B, Smith GS, Kimble W, Halasz A, Guthrie S, Fraustino JD, Hodder SL. Enhanced SARS-CoV-2 case prediction using public health data and machine learning models. JAMIA Open 2024; 7:ooae014. [PMID: 38444986 PMCID: PMC10913390 DOI: 10.1093/jamiaopen/ooae014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 01/29/2024] [Accepted: 02/08/2024] [Indexed: 03/07/2024] Open
Abstract
Objectives The goal of this study is to propose and test a scalable framework for machine learning (ML) algorithms to predict near-term severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cases by incorporating and evaluating the impact of real-time dynamic public health data. Materials and Methods Data used in this study include patient-level results, procurement, and location information of all SARS-CoV-2 tests reported in West Virginia as part of their mandatory reporting system from January 2021 to March 2022. We propose a method for incorporating and comparing widely available public health metrics inside of a ML framework, specifically a long-short-term memory network, to forecast SARS-CoV-2 cases across various feature sets. Results Our approach provides better prediction of localized case counts and indicates the impact of the dynamic elements of the pandemic on predictions, such as the influence of the mixture of viral variants in the population and variable testing and vaccination rates during various eras of the pandemic. Discussion Utilizing real-time public health metrics, including estimated Rt from multiple SARS-CoV-2 variants, vaccination rates, and testing information, provided a significant increase in the accuracy of the model during the Omicron and Delta period, thus providing more precise forecasting of daily case counts at the county level. This work provides insights on the influence of various features on predictive performance in rural and non-rural areas. Conclusion Our proposed framework incorporates available public health metrics with operational data on the impact of testing, vaccination, and current viral variant mixtures in the population to provide a foundation for combining dynamic public health metrics and ML models to deliver forecasting and insights in healthcare domains. It also shows the importance of developing and deploying ML frameworks in rural settings.
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Affiliation(s)
- Bradley S Price
- Department of Management Information Systems, West Virginia University, Morgantown, WV 26505, United States
- West Virginia Clinical and Translational Science Institute, Morgantown, WV 26506, United States
| | - Maryam Khodaverdi
- West Virginia Clinical and Translational Science Institute, Morgantown, WV 26506, United States
| | - Brian Hendricks
- West Virginia Clinical and Translational Science Institute, Morgantown, WV 26506, United States
- Department of Epidemiology and Biostatistics, West Virginia University, Morgantown, WV 26505, United States
| | - Gordon S Smith
- West Virginia Clinical and Translational Science Institute, Morgantown, WV 26506, United States
- Department of Epidemiology and Biostatistics, West Virginia University, Morgantown, WV 26505, United States
| | - Wes Kimble
- West Virginia Clinical and Translational Science Institute, Morgantown, WV 26506, United States
| | - Adam Halasz
- School of Mathematics and Data Science, West Virginia University, Morgantown, WV 26506, United States
| | - Sara Guthrie
- Department of Sociology and Anthropology, West Virginia University, Morgantown, WV 26505, United States
| | - Julia D Fraustino
- Department of Strategic Communication, Reed College of Media, West Virginia University, Morgantown, WV 26505, United States
| | - Sally L Hodder
- West Virginia Clinical and Translational Science Institute, Morgantown, WV 26506, United States
- Department of Medicine, West Virginia University, Morgantown, WV 26506, United States
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10
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Garg A, Alag S, Duncan D. CoSev: Data-Driven Optimizations for COVID-19 Severity Assessment in Low-Sample Regimes. Diagnostics (Basel) 2024; 14:337. [PMID: 38337853 PMCID: PMC10855975 DOI: 10.3390/diagnostics14030337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 01/06/2024] [Accepted: 01/19/2024] [Indexed: 02/12/2024] Open
Abstract
Given the pronounced impact COVID-19 continues to have on society-infecting 700 million reported individuals and causing 6.96 million deaths-many deep learning works have recently focused on the virus's diagnosis. However, assessing severity has remained an open and challenging problem due to a lack of large datasets, the large dimensionality of images for which to find weights, and the compute limitations of modern graphics processing units (GPUs). In this paper, a new, iterative application of transfer learning is demonstrated on the understudied field of 3D CT scans for COVID-19 severity analysis. This methodology allows for enhanced performance on the MosMed Dataset, which is a small and challenging dataset containing 1130 images of patients for five levels of COVID-19 severity (Zero, Mild, Moderate, Severe, and Critical). Specifically, given the large dimensionality of the input images, we create several custom shallow convolutional neural network (CNN) architectures and iteratively refine and optimize them, paying attention to learning rates, layer types, normalization types, filter sizes, dropout values, and more. After a preliminary architecture design, the models are systematically trained on a simplified version of the dataset-building models for two-class, then three-class, then four-class, and finally five-class classification. The simplified problem structure allows the model to start learning preliminary features, which can then be further modified for more difficult classification tasks. Our final model CoSev boosts classification accuracies from below 60% at first to 81.57% with the optimizations, reaching similar performance to the state-of-the-art on the dataset, with much simpler setup procedures. In addition to COVID-19 severity diagnosis, the explored methodology can be applied to general image-based disease detection. Overall, this work highlights innovative methodologies that advance current computer vision practices for high-dimension, low-sample data as well as the practicality of data-driven machine learning and the importance of feature design for training, which can then be implemented for improvements in clinical practices.
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Affiliation(s)
- Aksh Garg
- Computer Science Department, Stanford University, Stanford, CA 94305, USA; (A.G.); (S.A.)
| | - Shray Alag
- Computer Science Department, Stanford University, Stanford, CA 94305, USA; (A.G.); (S.A.)
| | - Dominique Duncan
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA 90033, USA
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Wang W, Harrou F, Dairi A, Sun Y. Stacked deep learning approach for efficient SARS-CoV-2 detection in blood samples. Artif Intell Med 2024; 148:102767. [PMID: 38325923 DOI: 10.1016/j.artmed.2024.102767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 01/02/2024] [Accepted: 01/05/2024] [Indexed: 02/09/2024]
Abstract
Identifying COVID-19 through blood sample analysis is crucial in managing the disease and improving patient outcomes. Despite its advantages, the current test demands certified laboratories, expensive equipment, trained personnel, and 3-4 h for results, with a notable false-negative rate of 15%-20%. This study proposes a stacked deep-learning approach for detecting COVID-19 in blood samples to distinguish uninfected individuals from those infected with the virus. Three stacked deep learning architectures, namely the StackMean, StackMax, and StackRF algorithms, are introduced to improve the detection quality of single deep learning models. To counter the class imbalance phenomenon in the training data, the Synthetic Minority Oversampling Technique (SMOTE) algorithm is also implemented, resulting in increased specificity and sensitivity. The efficacy of the methods is assessed by utilizing blood samples obtained from hospitals in Brazil and Italy. Results revealed that the StackMax method greatly boosted the deep learning and traditional machine learning methods' capability to distinguish COVID-19-positive cases from normal cases, while SMOTE increased the specificity and sensitivity of the stacked models. Hypothesis testing is performed to determine if there is a significant statistical difference in the performance between the compared detection methods. Additionally, the significance of blood sample features in identifying COVID-19 is analyzed using the XGBoost (eXtreme Gradient Boosting) technique for feature importance identification. Overall, this methodology could potentially enhance the timely and precise identification of COVID-19 in blood samples.
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Affiliation(s)
- Wu Wang
- Center for Applied Statistics and School of Statistics, Renmin University of China, Beijing 100872, China.
| | - Fouzi Harrou
- King Abdullah University of Science and Technology (KAUST), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Thuwal 23955-6900, Saudi Arabia.
| | - Abdelkader Dairi
- Computer Science Department, University of Science and Technology of Oran-Mohamed Boudiaf (USTO-MB), El Mnaouar, BP 1505, 31000, Bir El Djir, Algeria.
| | - Ying Sun
- King Abdullah University of Science and Technology (KAUST), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Thuwal 23955-6900, Saudi Arabia
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12
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Hussain S, Songhua X, Aslam MU, Hussain F. Clinical predictions of COVID-19 patients using deep stacking neural networks. J Investig Med 2024; 72:112-127. [PMID: 37712431 DOI: 10.1177/10815589231201103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/16/2023]
Abstract
The coronavirus disease 2019 (COVID-19) pandemic, which emerged in late 2019, has caused millions of infections and fatalities globally, disrupting various aspects of human society, including socioeconomic, political, and educational systems. One of the key challenges during the COVID-19 pandemic is accurately predicting the clinical development and outcome of the infected patients. In response, scientists and medical professionals globally have mobilized to develop prognostic strategies such as risk scores, biomarkers, and machine learning models to predict the clinical course and outcomes of COVID-19 patients. In this contribution, we deployed a mathematical approach called matrix factorization feature selection to select the most relevant features from the anonymized laboratory biomarkers and demographic data of COVID-19 patients. Based on these features, developed a model that leverages the deep stacking neural network (DSNN) to aid in clinical care by predicting patients' mortality risk. To gauge the performance of our suggested model, performed a comparative analysis with principal component analysis plus support vector machine, deep learning, and random forest, achieving outstanding performances. The DSNN model outperformed all the other models in terms of area under the curve (96.0%), F1-score (98.1%), recall (98.5%), accuracy (99.0%), precision (97.7%), specificity (97.0%), and maximum probability of correction decision (93.4%). Our model outperforms the clinical predictive models regarding patient mortality risk and classification in the literature. Therefore, we conclude that our robust model can help healthcare professionals to manage COVID-19 patients more effectively. We expect that early prediction of COVID-19 patients and preventive interventions can reduce the mortality risk of patients.
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Affiliation(s)
- Sajid Hussain
- School of Mathematics and Statistics XJTU, Xian, Shaanxi, China
| | - Xu Songhua
- School of Mathematics and Statistics XJTU, Xian, Shaanxi, China
| | | | - Fida Hussain
- School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey, Nuevo León, Mexico
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13
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Mann M, Badoni RP, Soni H, Al-Shehri M, Kaushik AC, Wei DQ. Utilization of Deep Convolutional Neural Networks for Accurate Chest X-Ray Diagnosis and Disease Detection. Interdiscip Sci 2023; 15:374-392. [PMID: 36966476 PMCID: PMC10040177 DOI: 10.1007/s12539-023-00562-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 03/06/2023] [Accepted: 03/06/2023] [Indexed: 03/27/2023]
Abstract
Chest radiography is a widely used diagnostic imaging procedure in medical practice, which involves prompt reporting of future imaging tests and diagnosis of diseases in the images. In this study, a critical phase in the radiology workflow is automated using the three convolutional neural network (CNN) models, viz. DenseNet121, ResNet50, and EfficientNetB1 for fast and accurate detection of 14 class labels of thoracic pathology diseases based on chest radiography. These models were evaluated on an AUC score for normal versus abnormal chest radiographs using 112120 chest X-ray14 datasets containing various class labels of thoracic pathology diseases to predict the probability of individual diseases and warn clinicians of potential suspicious findings. With DenseNet121, the AUROC scores for hernia and emphysema were predicted as 0.9450 and 0.9120, respectively. Compared to the score values obtained for each class on the dataset, the DenseNet121 outperformed the other two models. This article also aims to develop an automated server to capture fourteen thoracic pathology disease results using a tensor processing unit (TPU). The results of this study demonstrate that our dataset can be used to train models with high diagnostic accuracy for predicting the likelihood of 14 different diseases in abnormal chest radiographs, enabling accurate and efficient discrimination between different types of chest radiographs. This has the potential to bring benefits to various stakeholders and improve patient care.
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Affiliation(s)
- Mukesh Mann
- Department of Computer Science and Engineering, Indian Institute of Information Technology, Sonepat, Haryana 131029 India
| | - Rakesh P. Badoni
- Department of Mathematics, École Centrale School of Engineering, Mahindra University, Hyderabad, 500043 India
| | - Harsh Soni
- Department of Information Technology, Indian Institute of Information Technology, Sonepat, Haryana, 131029 India
| | - Mohammed Al-Shehri
- Department of Biology, Faculty of Science, King Khalid University, Abha, Saudi Arabia
| | - Aman Chandra Kaushik
- State Key Laboratory of Microbial Metabolism, and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 200030 Shanghai, China
- School of Biomedical Informatics, University of Texas Health Science Centre at Houston, Houston, TX USA
| | - Dong-Qing Wei
- State Key Laboratory of Microbial Metabolism, and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 200030 Shanghai, China
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14
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Li H, Yuan J, Fennell G, Abdulla V, Nistala R, Dandachi D, Ho DKC, Zhang Y. Recent advances in wearable sensors and data analytics for continuous monitoring and analysis of biomarkers and symptoms related to COVID-19. BIOPHYSICS REVIEWS 2023; 4:031302. [PMID: 38510705 PMCID: PMC10903389 DOI: 10.1063/5.0140900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 05/19/2023] [Indexed: 03/22/2024]
Abstract
The COVID-19 pandemic has changed the lives of many people around the world. Based on the available data and published reports, most people diagnosed with COVID-19 exhibit no or mild symptoms and could be discharged home for self-isolation. Considering that a substantial portion of them will progress to a severe disease requiring hospitalization and medical management, including respiratory and circulatory support in the form of supplemental oxygen therapy, mechanical ventilation, vasopressors, etc. The continuous monitoring of patient conditions at home for patients with COVID-19 will allow early determination of disease severity and medical intervention to reduce morbidity and mortality. In addition, this will allow early and safe hospital discharge and free hospital beds for patients who are in need of admission. In this review, we focus on the recent developments in next-generation wearable sensors capable of continuous monitoring of disease symptoms, particularly those associated with COVID-19. These include wearable non/minimally invasive biophysical (temperature, respiratory rate, oxygen saturation, heart rate, and heart rate variability) and biochemical (cytokines, cortisol, and electrolytes) sensors, sensor data analytics, and machine learning-enabled early detection and medical intervention techniques. Together, we aim to inspire the future development of wearable sensors integrated with data analytics, which serve as a foundation for disease diagnostics, health monitoring and predictions, and medical interventions.
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Affiliation(s)
- Huijie Li
- Department of Biomedical Engineering and the Institute of Materials Science, University of Connecticut, Storrs, Connecticut 06269, USA
| | - Jianhe Yuan
- Department of Electrical Engineering and Computer Science, University of Missouri-Columbia, Columbia, Missouri 65211, USA
| | - Gavin Fennell
- Department of Biomedical Engineering and the Institute of Materials Science, University of Connecticut, Storrs, Connecticut 06269, USA
| | - Vagif Abdulla
- Department of Biomedical Engineering and the Institute of Materials Science, University of Connecticut, Storrs, Connecticut 06269, USA
| | - Ravi Nistala
- Division of Nephrology, Department of Medicine, University of Missouri-Columbia, Columbia, Missouri 65212, USA
| | - Dima Dandachi
- Division of Infectious Diseases, Department of Medicine, University of Missouri-Columbia, 1 Hospital Drive, Columbia, Missouri 65212, USA
| | - Dominic K. C. Ho
- Department of Electrical Engineering and Computer Science, University of Missouri-Columbia, Columbia, Missouri 65211, USA
| | - Yi Zhang
- Department of Biomedical Engineering and the Institute of Materials Science, University of Connecticut, Storrs, Connecticut 06269, USA
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15
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Lam A, Squires E, Tan S, Swen NJ, Barilla A, Kovoor J, Gupta A, Bacchi S, Khurana S. Artificial intelligence for predicting acute appendicitis: a systematic review. ANZ J Surg 2023; 93:2070-2078. [PMID: 37458222 DOI: 10.1111/ans.18610] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 07/06/2023] [Indexed: 09/16/2023]
Abstract
BACKGROUND Paediatric appendicitis may be challenging to diagnose, and outcomes difficult to predict. While diagnostic and prognostic scores exist, artificial intelligence (AI) may be able to assist with these tasks. METHOD A systematic review was conducted aiming to evaluate the currently available evidence regarding the use of AI in the diagnosis and prognostication of paediatric appendicitis. In accordance with the PRISMA guidelines, the databases PubMed, EMBASE, and Cochrane Library were searched. This review was prospectively registered on PROSPERO. RESULTS Ten studies met inclusion criteria. All studies described the derivation and validation of AI models, and none described evaluation of the implementation of these models. Commonly used input parameters included varying combinations of demographic, clinical, laboratory, and imaging characteristics. While multiple studies used histopathological examination as the ground truth for a diagnosis of appendicitis, less robust techniques, such as the use of ICD10 codes, were also employed. Commonly used algorithms have included random forest models and artificial neural networks. High levels of model performance have been described for diagnosis of appendicitis and, to a lesser extent, subtypes of appendicitis (such as complicated versus uncomplicated appendicitis). Most studies did not provide all measures of model performance required to assess clinical usability. CONCLUSIONS The available evidence suggests the creation of prediction models for diagnosis and classification of appendicitis using AI techniques, is being increasingly explored. However, further implementation studies are required to demonstrate benefit in system or patient-centred outcomes with model deployment and to progress these models to the stage of clinical usability.
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Affiliation(s)
- Antoinette Lam
- University of Adelaide, Adelaide, South Australia, Australia
| | - Emily Squires
- Flinders University, Adelaide, South Australia, Australia
| | - Sheryn Tan
- University of Adelaide, Adelaide, South Australia, Australia
| | - Ng Jeng Swen
- University of Adelaide, Adelaide, South Australia, Australia
| | | | - Joshua Kovoor
- University of Adelaide, Adelaide, South Australia, Australia
- Royal Adelaide Hospital, Adelaide, South Australia, Australia
- Gold Coast University Hospital, Gold Coast, Queensland, Australia
| | - Aashray Gupta
- University of Adelaide, Adelaide, South Australia, Australia
- Women's and Children's Hospital, Adelaide, South Australia, Australia
| | - Stephen Bacchi
- University of Adelaide, Adelaide, South Australia, Australia
- Flinders University, Adelaide, South Australia, Australia
- Royal Adelaide Hospital, Adelaide, South Australia, Australia
| | - Sanjeev Khurana
- University of Adelaide, Adelaide, South Australia, Australia
- Women's and Children's Hospital, Adelaide, South Australia, Australia
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16
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Su X, Sun Y, Liu H, Lang Q, Zhang Y, Zhang J, Wang C, Chen Y. An innovative ensemble model based on deep learning for predicting COVID-19 infection. Sci Rep 2023; 13:12322. [PMID: 37516796 PMCID: PMC10387055 DOI: 10.1038/s41598-023-39408-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 07/25/2023] [Indexed: 07/31/2023] Open
Abstract
Nowadays, global public health crises are occurring more frequently, and accurate prediction of these diseases can reduce the burden on the healthcare system. Taking COVID-19 as an example, accurate prediction of infection can assist experts in effectively allocating medical resources and diagnosing diseases. Currently, scholars worldwide use single model approaches or epidemiology models more often to predict the outbreak trend of COVID-19, resulting in poor prediction accuracy. Although a few studies have employed ensemble models, there is still room for improvement in their performance. In addition, there are only a few models that use the laboratory results of patients to predict COVID-19 infection. To address these issues, research efforts should focus on improving disease prediction performance and expanding the use of medical disease prediction models. In this paper, we propose an innovative deep learning model Whale Optimization Convolutional Neural Networks (CNN), Long-Short Term Memory (LSTM) and Artificial Neural Network (ANN) called WOCLSA which incorporates three models ANN, CNN and LSTM. The WOCLSA model utilizes the Whale Optimization Algorithm to optimize the neuron number, dropout and batch size parameters in the integrated model of ANN, CNN and LSTM, thereby finding the global optimal solution parameters. WOCLSA employs 18 patient indicators as predictors, and compares its results with three other ensemble deep learning models. All models were validated with train-test split approaches. We evaluate and compare our proposed model and other models using accuracy, F1 score, recall, AUC and precision metrics. Through many studies and tests, our results show that our prediction models can identify patients with COVID-19 infection at the AUC of 91%, 91%, and 93% respectively. Other prediction results achieve a respectable accuracy of 92.82%, 92.79%, and 91.66% respectively, f1-score of 93.41%, 92.79%, and 92.33% respectively, precision of 93.41%, 92.79%, and 92.33% respectively, recall of 93.41%, 92.79%, and 92.33% respectively. All of these exceed 91%, surpassing those of comparable models. The execution time of WOCLSA is also an advantage. Therefore, the WOCLSA ensemble model can be used to assist in verifying laboratory research results and predict and to judge various diseases in public health events.
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Affiliation(s)
- Xiaoying Su
- School of Jilin Emergency Management, Changchun Institute of Technology, Changchun, 130021, China
| | - Yanfeng Sun
- College of Computer Science and Technology, Jilin University, Changchun, 130012, China
| | - Hongxi Liu
- School of Jilin Emergency Management, Changchun Institute of Technology, Changchun, 130021, China
| | - Qiuling Lang
- School of Jilin Emergency Management, Changchun Institute of Technology, Changchun, 130021, China
| | - Yichen Zhang
- School of Jilin Emergency Management, Changchun Institute of Technology, Changchun, 130021, China
| | - Jiquan Zhang
- School of Environment, Northeast Normal University, Changchun, 130024, China
| | - Chaoyong Wang
- School of Jilin Emergency Management, Changchun Institute of Technology, Changchun, 130021, China.
| | - Yanan Chen
- School of Jilin Emergency Management, Changchun Institute of Technology, Changchun, 130021, China
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17
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Dou B, Zhu Z, Merkurjev E, Ke L, Chen L, Jiang J, Zhu Y, Liu J, Zhang B, Wei GW. Machine Learning Methods for Small Data Challenges in Molecular Science. Chem Rev 2023; 123:8736-8780. [PMID: 37384816 PMCID: PMC10999174 DOI: 10.1021/acs.chemrev.3c00189] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/01/2023]
Abstract
Small data are often used in scientific and engineering research due to the presence of various constraints, such as time, cost, ethics, privacy, security, and technical limitations in data acquisition. However, big data have been the focus for the past decade, small data and their challenges have received little attention, even though they are technically more severe in machine learning (ML) and deep learning (DL) studies. Overall, the small data challenge is often compounded by issues, such as data diversity, imputation, noise, imbalance, and high-dimensionality. Fortunately, the current big data era is characterized by technological breakthroughs in ML, DL, and artificial intelligence (AI), which enable data-driven scientific discovery, and many advanced ML and DL technologies developed for big data have inadvertently provided solutions for small data problems. As a result, significant progress has been made in ML and DL for small data challenges in the past decade. In this review, we summarize and analyze several emerging potential solutions to small data challenges in molecular science, including chemical and biological sciences. We review both basic machine learning algorithms, such as linear regression, logistic regression (LR), k-nearest neighbor (KNN), support vector machine (SVM), kernel learning (KL), random forest (RF), and gradient boosting trees (GBT), and more advanced techniques, including artificial neural network (ANN), convolutional neural network (CNN), U-Net, graph neural network (GNN), Generative Adversarial Network (GAN), long short-term memory (LSTM), autoencoder, transformer, transfer learning, active learning, graph-based semi-supervised learning, combining deep learning with traditional machine learning, and physical model-based data augmentation. We also briefly discuss the latest advances in these methods. Finally, we conclude the survey with a discussion of promising trends in small data challenges in molecular science.
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Affiliation(s)
- Bozheng Dou
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences,Wuhan Textile University, Wuhan 430200, P, R. China
| | - Zailiang Zhu
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences,Wuhan Textile University, Wuhan 430200, P, R. China
| | - Ekaterina Merkurjev
- Department of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Lu Ke
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences,Wuhan Textile University, Wuhan 430200, P, R. China
| | - Long Chen
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences,Wuhan Textile University, Wuhan 430200, P, R. China
| | - Jian Jiang
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences,Wuhan Textile University, Wuhan 430200, P, R. China
- Department of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Yueying Zhu
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences,Wuhan Textile University, Wuhan 430200, P, R. China
| | - Jie Liu
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences,Wuhan Textile University, Wuhan 430200, P, R. China
| | - Bengong Zhang
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences,Wuhan Textile University, Wuhan 430200, P, R. China
| | - Guo-Wei Wei
- Department of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
- Department of Electrical and Computer Engineering, Michigan State University, East Lansing, Michigan 48824, United States
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan 48824, United States
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18
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Application of a novel deep learning technique using CT images for COVID-19 diagnosis on embedded systems. ALEXANDRIA ENGINEERING JOURNAL 2023; 74:345-358. [PMCID: PMC10183629 DOI: 10.1016/j.aej.2023.05.036] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Revised: 04/24/2023] [Accepted: 05/08/2023] [Indexed: 11/04/2023]
Abstract
Problem A novel coronavirus (COVID-19) has created a worldwide pneumonia epidemic, and it's important to make a computer-aided way for doctors to use computed tomography (CT) images to find people with COVID-19 as soon as possible. Aim: A fully automated, novel deep-learning method for diagnosis and prognostic analysis of COVID-19 on the embedded system is presented. Methods In this study, CT scans are utilized to identify individuals with COVID-19, pneumonia, or normal class. To achieve classification two pre-trained CNN models, namely ResNet50 and MobileNetv2, which are commonly used for image classification tasks. Additionally, a novel CNN architecture called CovidxNet-CT is introduced specifically designed for COVID-19 diagnosis using three classes of CT scans. To evaluate the effectiveness of the proposed method, k-fold cross-validation is employed, which is a common approach to estimate the performance of deep learning. The study is also evaluated the proposed method on two embedded system platforms, Jetson Nano and Tx2, to demonstrate its feasibility for deployment in resource-constrained environments. Results With an average accuracy of %98.83 and an AUC of 0.988, the system is trained and verified using a 4 fold cross-validation approach. Conclusion The optimistic outcomes from the investigation propose that CovidxNet-CT has the capacity to support radiologists and contribute towards the efforts to combat COVID-19. This study proposes a fully automated, deep-learning-based method for COVID-19 diagnosis and prognostic analysis that is specifically designed for use on embedded systems.
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19
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Dubey AK, Chabert GL, Carriero A, Pasche A, Danna PSC, Agarwal S, Mohanty L, Sharma N, Yadav S, Jain A, Kumar A, Kalra MK, Sobel DW, Laird JR, Singh IM, Singh N, Tsoulfas G, Fouda MM, Alizad A, Kitas GD, Khanna NN, Viskovic K, Kukuljan M, Al-Maini M, El-Baz A, Saba L, Suri JS. Ensemble Deep Learning Derived from Transfer Learning for Classification of COVID-19 Patients on Hybrid Deep-Learning-Based Lung Segmentation: A Data Augmentation and Balancing Framework. Diagnostics (Basel) 2023; 13:diagnostics13111954. [PMID: 37296806 DOI: 10.3390/diagnostics13111954] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 05/22/2023] [Accepted: 05/29/2023] [Indexed: 06/12/2023] Open
Abstract
BACKGROUND AND MOTIVATION Lung computed tomography (CT) techniques are high-resolution and are well adopted in the intensive care unit (ICU) for COVID-19 disease control classification. Most artificial intelligence (AI) systems do not undergo generalization and are typically overfitted. Such trained AI systems are not practical for clinical settings and therefore do not give accurate results when executed on unseen data sets. We hypothesize that ensemble deep learning (EDL) is superior to deep transfer learning (TL) in both non-augmented and augmented frameworks. METHODOLOGY The system consists of a cascade of quality control, ResNet-UNet-based hybrid deep learning for lung segmentation, and seven models using TL-based classification followed by five types of EDL's. To prove our hypothesis, five different kinds of data combinations (DC) were designed using a combination of two multicenter cohorts-Croatia (80 COVID) and Italy (72 COVID and 30 controls)-leading to 12,000 CT slices. As part of generalization, the system was tested on unseen data and statistically tested for reliability/stability. RESULTS Using the K5 (80:20) cross-validation protocol on the balanced and augmented dataset, the five DC datasets improved TL mean accuracy by 3.32%, 6.56%, 12.96%, 47.1%, and 2.78%, respectively. The five EDL systems showed improvements in accuracy of 2.12%, 5.78%, 6.72%, 32.05%, and 2.40%, thus validating our hypothesis. All statistical tests proved positive for reliability and stability. CONCLUSION EDL showed superior performance to TL systems for both (a) unbalanced and unaugmented and (b) balanced and augmented datasets for both (i) seen and (ii) unseen paradigms, validating both our hypotheses.
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Affiliation(s)
- Arun Kumar Dubey
- Bharati Vidyapeeth's College of Engineering, New Delhi 110063, India
| | - Gian Luca Chabert
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09123 Cagliari, Italy
| | - Alessandro Carriero
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09123 Cagliari, Italy
| | - Alessio Pasche
- Department of Radiology, "Maggiore della Carità" Hospital, University of Piemonte Orientale, Via Solaroli 17, 28100 Novara, Italy
| | - Pietro S C Danna
- Department of Radiology, "Maggiore della Carità" Hospital, University of Piemonte Orientale, Via Solaroli 17, 28100 Novara, Italy
| | - Sushant Agarwal
- Advanced Knowledge Engineering Centre, GBTI, Roseville, CA 95661, USA
| | - Lopamudra Mohanty
- ABES Engineering College, Ghaziabad 201009, India
- Department of Computer Science Engineering, Bennett University, Greater Noida 201310, India
| | - Neeraj Sharma
- School of Biomedical Engineering, Indian Institute of Technology (BHU), Varanasi 221005, India
| | - Sarita Yadav
- Bharati Vidyapeeth's College of Engineering, New Delhi 110063, India
| | - Achin Jain
- Bharati Vidyapeeth's College of Engineering, New Delhi 110063, India
| | - Ashish Kumar
- Department of Computer Science Engineering, Bennett University, Greater Noida 201310, India
| | - Mannudeep K Kalra
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02115, USA
| | - David W Sobel
- Men's Health Centre, Miriam Hospital Providence, Providence, RI 02906, USA
| | - John R Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St. Helena, CA 94574, USA
| | - Inder M Singh
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA
| | - Narpinder Singh
- Department of Food Science and Technology, Graphic Era, Deemed to be University, Dehradun 248002, India
| | - George Tsoulfas
- Department of Surgery, Aristoteleion University of Thessaloniki, 54124 Thessaloniki, Greece
| | - Mostafa M Fouda
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID 83209, USA
| | - Azra Alizad
- Department of Physiology & Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA
| | - George D Kitas
- Academic Affairs, Dudley Group NHS Foundation Trust, Dudley DY1 2HQ, UK
| | - Narendra N Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi 110001, India
| | - Klaudija Viskovic
- Department of Radiology and Ultrasound, University Hospital for Infectious Diseases, 10000 Zagreb, Croatia
| | - Melita Kukuljan
- Department of Interventional and Diagnostic Radiology, Clinical Hospital Center Rijeka, 51000 Rijeka, Croatia
| | - Mustafa Al-Maini
- Allergy, Clinical Immunology & Rheumatology Institute, Toronto, ON L4Z 4C4, Canada
| | - Ayman El-Baz
- Biomedical Engineering Department, University of Louisville, Louisville, KY 40292, USA
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09123 Cagliari, Italy
| | - Jasjit S Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA
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20
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Erol Doğan G, Uzbaş B. Diagnosis of COVID-19 from blood parameters using convolutional neural network. Soft comput 2023; 27:1-16. [PMID: 37362276 PMCID: PMC10225057 DOI: 10.1007/s00500-023-08508-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/10/2023] [Indexed: 06/28/2023]
Abstract
Asymptomatically presenting COVID-19 complicates the detection of infected individuals. Additionally, the virus changes too many genomic variants, which increases the virus's ability to spread. Because there isn't a specific treatment for COVID-19 in a short time, the essential goal is to reduce the virulence of the disease. Blood parameters, which contain essential clinical information about infectious diseases and are easy to access, have an important place in COVID-19 detection. The convolutional neural network (CNN) architecture, which is popular in image processing, produces highly successful results for COVID-19 detection models. When the literature is examined, it is seen that COVID-19 studies with CNN are generally done using lung images. In this study, one-dimensional (1D) blood parameters data were converted into two-dimensional (2D) image data after preprocessing, and COVID-19 detection was made with CNN. The t-distributed stochastic neighbor embedding method was applied to transfer the feature vectors to the 2D plane. All data were framed with convex hull and minimum bounding rectangle algorithms to obtain image data. The image data obtained by pixel mapping was presented to the developed 3-line CNN architecture. This study proposes an effective and successful model by providing a combination of low-cost and rapidly-accessible blood parameters and CNN architecture making image data processing highly successful for COVID-19 detection. Ultimately, COVID-19 detection was made with a success rate of 94.85%. This study has brought a new perspective to COVID-19 detection studies by obtaining 2D image data from 1D COVID-19 blood parameters and using CNN.
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Affiliation(s)
| | - Betül Uzbaş
- Computer Engineering Department, Konya Technical University, Konya, Turkey
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21
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Topuz K, Davazdahemami B, Delen D. A Bayesian belief network-based analytics methodology for early-stage risk detection of novel diseases. ANNALS OF OPERATIONS RESEARCH 2023:1-25. [PMID: 37361089 PMCID: PMC10189691 DOI: 10.1007/s10479-023-05377-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 05/01/2023] [Indexed: 06/28/2023]
Abstract
During a pandemic, medical specialists have substantial challenges in discovering and validating new disease risk factors and designing effective treatment strategies. Traditionally, this approach entails several clinical studies and trials that might last several years, during which strict preventive measures are enforced to manage the outbreak and limit the death toll. Advanced data analytics technologies, on the other hand, could be utilized to monitor and expedite the procedure. This research integrates evolutionary search algorithms, Bayesian belief networks, and innovative interpretation techniques to provide a comprehensive exploratory-descriptive-explanatory machine learning methodology to assist clinical decision-makers in responding promptly to pandemic scenarios. The proposed approach is illustrated through a case study in which the survival of COVID-19 patients is determined using inpatient and emergency department (ED) encounters from a real-world electronic health record database. Following an exploratory phase in which genetic algorithms are used to identify a set of the most critical chronic risk factors and their validation using descriptive tools based on the concept of Bayesian Belief Nets, the framework develops and trains a probabilistic graphical model to explain and predict patient survival (with an AUC of 0.92). Finally, a publicly available online, probabilistic decision support inference simulator was constructed to facilitate what-if analysis and aid general users and healthcare professionals in interpreting model findings. The results widely corroborate intensive and expensive clinical trial research assessments.
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Affiliation(s)
- Kazim Topuz
- Collins College of Business, School of Finance and Operations Management, The University of Tulsa, Tulsa, USA
| | - Behrooz Davazdahemami
- Department of IT and Supply Chain Management, University of Wisconsin-Whitewater, 809 W. Starin Rd., Hyland Hall 1222, Whitewater, USA
| | - Dursun Delen
- Center for Health Systems Innovation, Spears School of Business, Oklahoma State University, Stillwater, USA
- Faculty of Engineering and Natural Sciences, Istinye University, Istanbul, Turkey
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22
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Abbasi Habashi S, Koyuncu M, Alizadehsani R. A Survey of COVID-19 Diagnosis Using Routine Blood Tests with the Aid of Artificial Intelligence Techniques. Diagnostics (Basel) 2023; 13:1749. [PMID: 37238232 PMCID: PMC10217633 DOI: 10.3390/diagnostics13101749] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 04/19/2023] [Accepted: 04/29/2023] [Indexed: 05/28/2023] Open
Abstract
Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), causing a disease called COVID-19, is a class of acute respiratory syndrome that has considerably affected the global economy and healthcare system. This virus is diagnosed using a traditional technique known as the Reverse Transcription Polymerase Chain Reaction (RT-PCR) test. However, RT-PCR customarily outputs a lot of false-negative and incorrect results. Current works indicate that COVID-19 can also be diagnosed using imaging resolutions, including CT scans, X-rays, and blood tests. Nevertheless, X-rays and CT scans cannot always be used for patient screening because of high costs, radiation doses, and an insufficient number of devices. Therefore, there is a requirement for a less expensive and faster diagnostic model to recognize the positive and negative cases of COVID-19. Blood tests are easily performed and cost less than RT-PCR and imaging tests. Since biochemical parameters in routine blood tests vary during the COVID-19 infection, they may supply physicians with exact information about the diagnosis of COVID-19. This study reviewed some newly emerging artificial intelligence (AI)-based methods to diagnose COVID-19 using routine blood tests. We gathered information about research resources and inspected 92 articles that were carefully chosen from a variety of publishers, such as IEEE, Springer, Elsevier, and MDPI. Then, these 92 studies are classified into two tables which contain articles that use machine Learning and deep Learning models to diagnose COVID-19 while using routine blood test datasets. In these studies, for diagnosing COVID-19, Random Forest and logistic regression are the most widely used machine learning methods and the most widely used performance metrics are accuracy, sensitivity, specificity, and AUC. Finally, we conclude by discussing and analyzing these studies which use machine learning and deep learning models and routine blood test datasets for COVID-19 detection. This survey can be the starting point for a novice-/beginner-level researcher to perform on COVID-19 classification.
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Affiliation(s)
| | - Murat Koyuncu
- Department of Information Systems Engineering, Atilim University, 06830 Ankara, Turkey;
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, Geelong, VIC 3216, Australia
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23
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Karbasi Z, Gohari SH, Sabahi A. Bibliometric analysis of the use of artificial intelligence in COVID-19 based on scientific studies. Health Sci Rep 2023; 6:e1244. [PMID: 37152228 PMCID: PMC10158785 DOI: 10.1002/hsr2.1244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 04/11/2023] [Accepted: 04/16/2023] [Indexed: 05/09/2023] Open
Abstract
Background and Aims One such strategy is citation analysis used by researchers for research planning an article referred to by another article receives a "citation." By using bibliometric analysis, the development of research areas and authors' influence can be investigated. The current study aimed to identify and analyze the characteristics of 100 highly cited articles on the use of artificial intelligence concerning COVID-19. Methods On July 27, 2022, this database was searched using the keywords "artificial intelligence" and "COVID-19" in the topic. After extensive searching, all retrieved articles were sorted by the number of citations, and 100 highly cited articles were included based on the number of citations. The following data were extracted: year of publication, type of study, name of journal, country, number of citations, language, and keywords. Results The average number of citations for 100 highly cited articles was 138.54. The top three cited articles with 745, 596, and 549 citations. The top 100 articles were all in English and were published in 2020 and 2021. China was the most prolific country with 19 articles, followed by the United States with 15 articles and India with 10 articles. Conclusion The current bibliometric analysis demonstrated the significant growth of the use of artificial intelligence for COVID-19. Using these results, research priorities are more clearly defined, and researchers can focus on hot topics.
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Affiliation(s)
- Zahra Karbasi
- Medical Informatics Research Center, Institute for Futures Studies in HealthKerman University of Medical SciencesKermanIran
- Department of Health Information Sciences, Faculty of Management and Medical Information SciencesKerman University of Medical SciencesKermanIran
| | - Sadrieh H. Gohari
- Medical Informatics Research Center, Institute for Futures Studies in HealthKerman University of Medical SciencesKermanIran
| | - Azam Sabahi
- Department of Health Information Technology, Ferdows School of Health and Allied Medical SciencesBirjand University of Medical SciencesBirjandIran
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24
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Ortiz-Barrios M, Arias-Fonseca S, Ishizaka A, Barbati M, Avendaño-Collante B, Navarro-Jiménez E. Artificial intelligence and discrete-event simulation for capacity management of intensive care units during the Covid-19 pandemic: A case study. JOURNAL OF BUSINESS RESEARCH 2023; 160:113806. [PMID: 36895308 PMCID: PMC9981538 DOI: 10.1016/j.jbusres.2023.113806] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 01/18/2023] [Accepted: 02/23/2023] [Indexed: 06/18/2023]
Abstract
The Covid-19 pandemic has pushed the Intensive Care Units (ICUs) into significant operational disruptions. The rapid evolution of this disease, the bed capacity constraints, the wide variety of patient profiles, and the imbalances within health supply chains still represent a challenge for policymakers. This paper aims to use Artificial Intelligence (AI) and Discrete-Event Simulation (DES) to support ICU bed capacity management during Covid-19. The proposed approach was validated in a Spanish hospital chain where we initially identified the predictors of ICU admission in Covid-19 patients. Second, we applied Random Forest (RF) to predict ICU admission likelihood using patient data collected in the Emergency Department (ED). Finally, we included the RF outcomes in a DES model to assist decision-makers in evaluating new ICU bed configurations responding to the patient transfer expected from downstream services. The results evidenced that the median bed waiting time declined between 32.42 and 48.03 min after intervention.
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Affiliation(s)
- Miguel Ortiz-Barrios
- Department of Productivity and Innovation, Universidad de la Costa CUC, Barranquilla 080002, Colombia
| | - Sebastián Arias-Fonseca
- Department of Productivity and Innovation, Universidad de la Costa CUC, Barranquilla 080002, Colombia
| | - Alessio Ishizaka
- NEOMA Business School, 1 rue du Maréchal Juin, Mont-Saint-Aignan 76130, France
| | - Maria Barbati
- Department of Economics, University Ca' Foscari, Cannaregio 873, Fondamenta San Giobbe, 30121 Venice, Italy
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25
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Göreke V. A Novel Deep-Learning-Based CADx Architecture for Classification of Thyroid Nodules Using Ultrasound Images. Interdiscip Sci 2023:10.1007/s12539-023-00560-4. [PMID: 36976511 PMCID: PMC10043860 DOI: 10.1007/s12539-023-00560-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 03/03/2023] [Accepted: 03/05/2023] [Indexed: 03/29/2023]
Abstract
Nodules of thyroid cancer occur in the cells of the thyroid as benign or malign types. Thyroid sonographic images are mostly used for diagnosis of thyroid cancer. The aim of this study is to introduce a computer-aided diagnosis system that can classify the thyroid nodules with high accuracy using the data gathered from ultrasound images. Acquisition and labeling of sub-images were performed by a specialist physician. Then the number of these sub-images were increased using data augmentation methods. Deep features were obtained from the images using a pre-trained deep neural network. The dimensions of the features were reduced and features were improved. The improved features were combined with morphological and texture features. This feature group was rated by a value called similarity coefficient value which was obtained from a similarity coefficient generator module. The nodules were classified as benign or malignant using a multi-layer deep neural network with a pre-weighting layer designed with a novel approach. In this study, a novel multi-layer computer-aided diagnosis system was proposed for thyroid cancer detection. In the first layer of the system, a novel feature extraction method based on the class similarity of images was developed. In the second layer, a novel pre-weighting layer was proposed by modifying the genetic algorithm. The proposed system showed superior performance in different metrics compared to the literature.
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Affiliation(s)
- Volkan Göreke
- Department of Computer Technologies, Sivas Vocational School of Technical Sciences, Sivas Cumhuriyet University, 58140, Sivas, Türkiye.
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26
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Tenali N, Babu GRM. A Systematic Literature Review and Future Perspectives for Handling Big Data Analytics in COVID-19 Diagnosis. NEW GENERATION COMPUTING 2023; 41:243-280. [PMID: 37229177 PMCID: PMC10019802 DOI: 10.1007/s00354-023-00211-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Accepted: 02/23/2023] [Indexed: 05/27/2023]
Abstract
In today's digital world, information is growing along with the expansion of Internet usage worldwide. As a consequence, bulk of data is generated constantly which is known to be "Big Data". One of the most evolving technologies in twenty-first century is Big Data analytics, it is promising field for extracting knowledge from very large datasets and enhancing benefits while lowering costs. Due to the enormous success of big data analytics, the healthcare sector is increasingly shifting toward adopting these approaches to diagnose diseases. Due to the recent boom in medical big data and the development of computational methods, researchers and practitioners have gained the ability to mine and visualize medical big data on a larger scale. Thus, with the aid of integration of big data analytics in healthcare sectors, precise medical data analysis is now feasible with early sickness detection, health status monitoring, patient treatment, and community services is now achievable. With all these improvements, a deadly disease COVID is considered in this comprehensive review with the intention of offering remedies utilizing big data analytics. The use of big data applications is vital to managing pandemic conditions, such as predicting outbreaks of COVID-19 and identifying cases and patterns of spread of COVID-19. Research is still being done on leveraging big data analytics to forecast COVID-19. But precise and early identification of COVID disease is still lacking due to the volume of medical records like dissimilar medical imaging modalities. Meanwhile, Digital imaging has now become essential to COVID diagnosis, but the main challenge is the storage of massive volumes of data. Taking these limitations into account, a comprehensive analysis is presented in the systematic literature review (SLR) to provide a deeper understanding of big data in the field of COVID-19.
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Affiliation(s)
- Nagamani Tenali
- Department of CSE, Dr.Y.S. Rajasekhar Reddy University College of Engineering & Technology, Acharya Nagarjuna University, Nagarjuna Nagar, Guntur, India
| | - Gatram Rama Mohan Babu
- Computer Science and Engineering (AI&ML), RVR & JC College of Engineering, Chowdavaram, Guntur, India
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27
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Xue X, Chinnaperumal S, Abdulsahib GM, Manyam RR, Marappan R, Raju SK, Khalaf OI. Design and Analysis of a Deep Learning Ensemble Framework Model for the Detection of COVID-19 and Pneumonia Using Large-Scale CT Scan and X-ray Image Datasets. Bioengineering (Basel) 2023; 10:363. [PMID: 36978754 PMCID: PMC10045423 DOI: 10.3390/bioengineering10030363] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Revised: 03/09/2023] [Accepted: 03/10/2023] [Indexed: 03/18/2023] Open
Abstract
Recently, various methods have been developed to identify COVID-19 cases, such as PCR testing and non-contact procedures such as chest X-rays and computed tomography (CT) scans. Deep learning (DL) and artificial intelligence (AI) are critical tools for early and accurate detection of COVID-19. This research explores the different DL techniques for identifying COVID-19 and pneumonia on medical CT and radiography images using ResNet152, VGG16, ResNet50, and DenseNet121. The ResNet framework uses CT scan images with accuracy and precision. This research automates optimum model architecture and training parameters. Transfer learning approaches are also employed to solve content gaps and shorten training duration. An upgraded VGG16 deep transfer learning architecture is applied to perform multi-class classification for X-ray imaging tasks. Enhanced VGG16 has been proven to recognize three types of radiographic images with 99% accuracy, typical for COVID-19 and pneumonia. The validity and performance metrics of the proposed model were validated using publicly available X-ray and CT scan data sets. The suggested model outperforms competing approaches in diagnosing COVID-19 and pneumonia. The primary outcomes of this research result in an average F-score (95%, 97%). In the event of healthy viral infections, this research is more efficient than existing methodologies for coronavirus detection. The created model is appropriate for recognition and classification pre-training. The suggested model outperforms traditional strategies for multi-class categorization of various illnesses.
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Affiliation(s)
- Xingsi Xue
- Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou 350011, China
| | - Seelammal Chinnaperumal
- Department of Computer Science and Engineering, Solamalai College of Engineering, Madurai 625020, Tamil Nadu, India
| | | | - Rajasekhar Reddy Manyam
- Amrita School of Computing, Amrita Vishwa Vidyapeetham, Amaravati Campus, Mangalagiri 522503, Andhra Pradesh, India
| | - Raja Marappan
- School of Computing, SASTRA Deemed University, Thanjavur 613401, Tamil Nadu, India
| | - Sekar Kidambi Raju
- School of Computing, SASTRA Deemed University, Thanjavur 613401, Tamil Nadu, India
| | - Osamah Ibrahim Khalaf
- Department of Solar, Al-Nahrain Renewable Energy Research Center, Al-Nahrain University, Baghdad 64040, Iraq
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28
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Bhosale YH, Patnaik KS. Bio-medical imaging (X-ray, CT, ultrasound, ECG), genome sequences applications of deep neural network and machine learning in diagnosis, detection, classification, and segmentation of COVID-19: a Meta-analysis & systematic review. MULTIMEDIA TOOLS AND APPLICATIONS 2023:1-54. [PMID: 37362676 PMCID: PMC10015538 DOI: 10.1007/s11042-023-15029-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 02/01/2023] [Accepted: 02/27/2023] [Indexed: 06/28/2023]
Abstract
This review investigates how Deep Machine Learning (DML) has dealt with the Covid-19 epidemic and provides recommendations for future Covid-19 research. Despite the fact that vaccines for this epidemic have been developed, DL methods have proven to be a valuable asset in radiologists' arsenals for the automated assessment of Covid-19. This detailed review debates the techniques and applications developed for Covid-19 findings using DL systems. It also provides insights into notable datasets used to train neural networks, data partitioning, and various performance measurement metrics. The PRISMA taxonomy has been formed based on pretrained(45 systems) and hybrid/custom(17 systems) models with radiography modalities. A total of 62 systems with respect to X-ray(32), CT(19), ultrasound(7), ECG(2), and genome sequence(2) based modalities as taxonomy are selected from the studied articles. We originate by valuing the present phase of DL and conclude with significant limitations. The restrictions contain incomprehensibility, simplification measures, learning from incomplete labeled data, and data secrecy. Moreover, DML can be utilized to detect and classify Covid-19 from other COPD illnesses. The proposed literature review has found many DL-based systems to fight against Covid19. We expect this article will assist in speeding up the procedure of DL for Covid-19 researchers, including medical, radiology technicians, and data engineers.
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Affiliation(s)
- Yogesh H. Bhosale
- Computer Science and Engineering Department, Birla Institute of Technology, Mesra, Ranchi, India
| | - K. Sridhar Patnaik
- Computer Science and Engineering Department, Birla Institute of Technology, Mesra, Ranchi, India
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29
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Deebak BD, Al-Turjman F. EEI-IoT: Edge-Enabled Intelligent IoT Framework for Early Detection of COVID-19 Threats. SENSORS (BASEL, SWITZERLAND) 2023; 23:2995. [PMID: 36991706 PMCID: PMC10051552 DOI: 10.3390/s23062995] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 12/11/2022] [Accepted: 12/30/2022] [Indexed: 06/19/2023]
Abstract
Coronavirus disease 2019 (COVID-19) has caused severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) across the globe, impacting effective diagnosis and treatment for any chronic illnesses and long-term health implications. In this worldwide crisis, the pandemic shows its daily extension (i.e., active cases) and genome variants (i.e., Alpha) within the virus class and diversifies the association with treatment outcomes and drug resistance. As a consequence, healthcare-related data including instances of sore throat, fever, fatigue, cough, and shortness of breath are given due consideration to assess the conditional state of patients. To gain unique insights, wearable sensors can be implanted in a patient's body that periodically generates an analysis report of the vital organs to a medical center. However, it is still challenging to analyze risks and predict their related countermeasures. Therefore, this paper presents an intelligent Edge-IoT framework (IE-IoT) to detect potential threats (i.e., behavioral and environmental) in the early stage of the disease. The prime objective of this framework is to apply a new pre-trained deep learning model enabled by self-supervised transfer learning to build an ensemble-based hybrid learning model and to offer an effective analysis of prediction accuracy. To construct proper clinical symptoms, treatment, and diagnosis, an effective analysis such as STL observes the impact of the learning models such as ANN, CNN, and RNN. The experimental analysis proves that the ANN model considers the most effective features and attains a better accuracy (~98.3%) than other learning models. Also, the proposed IE-IoT can utilize the communication technologies of IoT such as BLE, Zigbee, and 6LoWPAN to examine the factor of power consumption. Above all, the real-time analysis reveals that the proposed IE-IoT with 6LoWPAN consumes less power and response time than the other state-of-the-art approaches to infer the suspected victims at an early stage of development of the disease.
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Affiliation(s)
- B. D. Deebak
- Department of Computer Engineering, Gachon University, Gyeonggido, Seongnam 13120, Republic of Korea
| | - Fadi Al-Turjman
- Artificial Intelligence Engineering Deptartment, AI and Robotics Institute, Near East University, Mersin 10, Turkey
- Research Center for AI and IoT, Faculty of Engineering, University of Kyrenia, Mersin 10, Turkey
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30
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Altantawy DA, Kishk SS. Equilibrium-based COVID-19 diagnosis from routine blood tests: A sparse deep convolutional model. EXPERT SYSTEMS WITH APPLICATIONS 2023; 213:118935. [PMID: 36210961 PMCID: PMC9527205 DOI: 10.1016/j.eswa.2022.118935] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 06/21/2022] [Accepted: 09/27/2022] [Indexed: 06/16/2023]
Abstract
SARS-CoV2 (COVID-19) is the virus that causes the pandemic that has severely impacted human society with a massive death toll worldwide. Hence, there is a persistent need for fast and reliable automatic tools to help health teams in making clinical decisions. Predictive models could potentially ease the strain on healthcare systems by early and reliable screening of COVID-19 patients which helps to combat the spread of the disease. Recent studies have reported some key advantages of employing routine blood tests for initial screening of COVID-19 patients. Thus, in this paper, we propose a novel COVID-19 prediction model based on routine blood tests. In this model, we depend on exploiting the real dependency among the employed feature pool by a sparsification procedure. In this sparse domain, a hybrid feature selection mechanism is proposed. This mechanism fuses the selected features from two perspectives, the first is Pearson correlation and the second is a new Minkowski-based equilibrium optimizer (MEO). Then, the selected features are fed into a new 1D Convolutional Neural Network (1DCNN) for a final diagnosis decision. The proposed prediction model is tested with a new public dataset from San Raphael Hospital, Milan, Italy, i.e., OSR dataset which has two sub-datasets. According to the experimental results, the proposed model outperforms the state-of-the-art techniques with an average testing accuracy of 98.5% while we employ only less than half the size of the feature pool, i.e., we need only less than half the given blood tests in the employed dataset to get a final diagnosis decision.
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Affiliation(s)
- Doaa A Altantawy
- Electronics and Communications Engineering Department, Faculty of Engineering, Mansoura University, 60 El-Gomhoria Street, Mansoura, Egypt
| | - Sherif S Kishk
- Electronics and Communications Engineering Department, Faculty of Engineering, Mansoura University, 60 El-Gomhoria Street, Mansoura, Egypt
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31
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Cao J, Xiao Y, Zhang M, Huang L, Wang Y, Liu W, Wang X, Wu J, Huang Y, Wang R, Zhou L, Li L, Zhang Y, Ren L, Qian K, Wang J. Deep Learning of Dual Plasma Fingerprints for High-Performance Infection Classification. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2023; 19:e2206349. [PMID: 36470664 DOI: 10.1002/smll.202206349] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 11/17/2022] [Indexed: 06/17/2023]
Abstract
Infection classification is the key for choosing the proper treatment plans. Early determination of the causative agents is critical for disease control. Host responses analysis can detect variform and sensitive host inflammatory responses to ascertain the presence and type of the infection. However, traditional host-derived inflammatory indicators are insufficient for clinical infection classification. Fingerprints-based omic analysis has attracted increasing attention globally for analyzing the complex host systemic immune response. A single type of fingerprints is not applicable for infection classification (area under curve (AUC) of 0.550-0.617). Herein, an infection classification platform based on deep learning of dual plasma fingerprints (DPFs-DL) is developed. The DPFs with high reproducibility (coefficient of variation <15%) are obtained at low sample consumption (550 nL native plasma) using inorganic nanoparticle and organic matrix assisted laser desorption/ionization mass spectrometry. A classifier (DPFs-DL) for viral versus bacterial infection discrimination (AUC of 0.775) and coronavirus disease 2019 (COVID-2019) diagnosis (AUC of 0.917) is also built. Furthermore, a metabolic biomarker panel of two differentially regulated metabolites, which may serve as potential biomarkers for COVID-19 management (AUC of 0.677-0.883), is constructed. This study will contribute to the development of precision clinical care for infectious diseases.
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Affiliation(s)
- Jing Cao
- State Key Laboratory for Oncogenes and Related Genes, School of Biomedical Engineering and Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
- Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Road, Shanghai, 200127, P. R. China
- Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore, 117583, Singapore
| | - Yan Xiao
- NHC Key Laboratory of Systems Biology of Pathogens and Christophe Merieux Laboratory, Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, P. R. China
- Key Laboratory of Respiratory Disease Pathogenomics, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, P. R. China
| | - Mengji Zhang
- State Key Laboratory for Oncogenes and Related Genes, School of Biomedical Engineering and Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
- Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Road, Shanghai, 200127, P. R. China
| | - Lin Huang
- Country Department of Clinical Laboratory Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
| | - Ying Wang
- NHC Key Laboratory of Systems Biology of Pathogens and Christophe Merieux Laboratory, Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, P. R. China
| | - Wanshan Liu
- State Key Laboratory for Oncogenes and Related Genes, School of Biomedical Engineering and Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
- Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Road, Shanghai, 200127, P. R. China
| | - Xinming Wang
- NHC Key Laboratory of Systems Biology of Pathogens and Christophe Merieux Laboratory, Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, P. R. China
| | - Jiao Wu
- State Key Laboratory for Oncogenes and Related Genes, School of Biomedical Engineering and Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
- Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Road, Shanghai, 200127, P. R. China
| | - Yida Huang
- State Key Laboratory for Oncogenes and Related Genes, School of Biomedical Engineering and Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
- Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Road, Shanghai, 200127, P. R. China
| | - Ruimin Wang
- State Key Laboratory for Oncogenes and Related Genes, School of Biomedical Engineering and Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
- Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Road, Shanghai, 200127, P. R. China
| | - Li Zhou
- Beijing health biotech co. Ltd, Beijing, 100193, P. R. China
| | - Lin Li
- Beijing health biotech co. Ltd, Beijing, 100193, P. R. China
| | - Yong Zhang
- Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore, 117583, Singapore
| | - Lili Ren
- NHC Key Laboratory of Systems Biology of Pathogens and Christophe Merieux Laboratory, Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, P. R. China
- Key Laboratory of Respiratory Disease Pathogenomics, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, P. R. China
| | - Kun Qian
- State Key Laboratory for Oncogenes and Related Genes, School of Biomedical Engineering and Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
- Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Road, Shanghai, 200127, P. R. China
| | - Jianwei Wang
- NHC Key Laboratory of Systems Biology of Pathogens and Christophe Merieux Laboratory, Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, P. R. China
- Key Laboratory of Respiratory Disease Pathogenomics, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, P. R. China
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Kim G, Lim H, Kim Y, Kwon O, Choi JH. Intra-person multi-task learning method for chronic-disease prediction. Sci Rep 2023; 13:1069. [PMID: 36658206 PMCID: PMC9851106 DOI: 10.1038/s41598-023-28383-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2022] [Accepted: 01/18/2023] [Indexed: 01/20/2023] Open
Abstract
In the medical field, various clinical information has been accumulated to help clinicians provide personalized medicine and make better diagnoses. As chronic diseases share similar characteristics, it is possible to predict multiple chronic diseases using the accumulated data of each patient. Thus, we propose an intra-person multi-task learning framework that jointly predicts the status of correlated chronic diseases and improves the model performance. Because chronic diseases occur over a long period and are affected by various factors, we considered features related to each chronic disease and the temporal relationship of the time-series data for accurate prediction. The study was carried out in three stages: (1) data preprocessing and feature selection using bidirectional recurrent imputation for time series (BRITS) and the least absolute shrinkage and selection operator (LASSO); (2) a convolutional neural network and long short-term memory (CNN-LSTM) for single-task models; and (3) a novel intra-person multi-task learning CNN-LSTM framework developed to predict multiple chronic diseases simultaneously. Our multi-task learning method between correlated chronic diseases produced a more stable and accurate system than single-task models and other baseline recurrent networks. Furthermore, the proposed model was tested using different time steps to illustrate its flexibility and generalization across multiple time steps.
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Affiliation(s)
- Gihyeon Kim
- Department of Computational Medicine, Graduate Program in System Health Science and Engineering, Ewha Womans University, Seoul, 03760, Korea
| | - Heeryung Lim
- Division of Mechanical and Biomedical Engineering, Graduate Program in System Health Science and Engineering, Ewha Womans University, Seoul, 03760, Korea
| | - Yunsoo Kim
- Department of Nutritional Science and Food Management, Graduate Program in System Health Science and Engineering, Ewha Womans University, Seoul, 03760, Korea
| | - Oran Kwon
- Department of Nutritional Science and Food Management, Graduate Program in System Health Science and Engineering, Ewha Womans University, Seoul, 03760, Korea
| | - Jang-Hwan Choi
- Division of Mechanical and Biomedical Engineering, Graduate Program in System Health Science and Engineering, Ewha Womans University, Seoul, 03760, Korea.
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Kumar Y, Koul A, Kaur S, Hu YC. Machine Learning and Deep Learning Based Time Series Prediction and Forecasting of Ten Nations' COVID-19 Pandemic. SN COMPUTER SCIENCE 2023; 4:91. [PMID: 36532634 PMCID: PMC9748400 DOI: 10.1007/s42979-022-01493-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 11/03/2022] [Indexed: 12/15/2022]
Abstract
In the paper, the authors investigated and predicted the future environmental circumstances of a COVID-19 to minimize its effects using artificial intelligence techniques. The experimental investigation of COVID-19 instances has been performed in ten countries, including India, the United States, Russia, Argentina, Brazil, Colombia, Italy, Turkey, Germany, and France using machine learning, deep learning, and time series models. The confirmed, deceased, and recovered datasets from January 22, 2020, to May 29, 2021, of Novel COVID-19 cases were considered from the Kaggle COVID dataset repository. The country-wise Exploratory Data Analysis visually represents the active, recovered, closed, and death cases from March 2020 to May 2021. The data are pre-processed and scaled using a MinMax scaler to extract and normalize the features to obtain an accurate prediction rate. The proposed methodology employs Random Forest Regressor, Decision Tree Regressor, K Nearest Regressor, Lasso Regression, Linear Regression, Bayesian Regression, Theilsen Regression, Kernel Ridge Regressor, RANSAC Regressor, XG Boost, Elastic Net Regressor, Facebook Prophet Model, Holt Model, Stacked Long Short-Term Memory, and Stacked Gated Recurrent Units to predict active COVID-19 confirmed, death, and recovered cases. Out of different machine learning, deep learning, and time series models, Random Forest Regressor, Facebook Prophet, and Stacked LSTM outperformed to predict the best results for COVID-19 instances with the lowest root-mean-square and highest R 2 score values.
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Affiliation(s)
- Yogesh Kumar
- Department of Computer Science and Engineering, School of Technology, Pandit Deendayal Energy University, Gandhinagar, Gujarat India
| | - Apeksha Koul
- Department of Computer Engineering, Punjabi University, Patiala, India
| | - Sukhpreet Kaur
- Department of Computer Science and Engineering, Chandigarh Engineering College, Landran, Mohali India
| | - Yu-Chen Hu
- Department of Computer Science and Information Management, Providence University, Taichung, Taiwan, ROC
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Ur Rehman A, Naseer A, Karim S, Tamoor M, Naz S. Deep learning classifiers for computer-aided diagnosis of multiple lungs disease. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2023; 31:1125-1143. [PMID: 37522236 DOI: 10.3233/xst-230113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/01/2023]
Abstract
BACKGROUND Computer aided diagnosis has gained momentum in the recent past. The advances in deep learning and availability of huge volumes of data along with increased computational capabilities has reshaped the diagnosis and prognosis procedures. OBJECTIVE These methods are proven to be relatively less expensive and safer alternatives of the otherwise traditional approaches. This study is focused on efficient diagnosis of three very common diseases: lung cancer, pneumonia and Covid-19 using X-ray images. METHODS Three different deep learning models are designed and developed to perform 4-way classification. Inception V3, Convolutional Neural Networks (CNN) and Long Short Term Memory models (LSTM) are used as building blocks. The performance of these models is evaluated using three publicly available datasets, the first dataset contains images for Lung cancer, second contains images for Covid-19 and third dataset contains images for Pneumonia and normal subjects. Combining three datasets creates a class imbalance problem which is resolved using pre-processing and data augmentation techniques. After data augmentation 1386 subjects are randomly chosen for each class. RESULTS It is observed that CNN when combined with LSTM (CNN-LSTM) produces significantly improved results (accuracy of 94.5 %) which is better than CNN and InceptionV3-LSTM. 3,5, and 10 fold cross validation is performed to verify all results calculated using three different classifiersConclusions:This research concludes that a single computer-aided diagnosis system can be developed for diagnosing multiple diseases.
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Affiliation(s)
- Aziz Ur Rehman
- National University of Computer and Emerging Science, Faisal Town, Lahore, Pakistan
| | - Asma Naseer
- National University of Computer and Emerging Science, Faisal Town, Lahore, Pakistan
| | - Saira Karim
- National University of Computer and Emerging Science, Faisal Town, Lahore, Pakistan
| | - Maria Tamoor
- Forman Christian College University, Zahoor Ilahi Road, Lahore, Pakistan
| | - Samina Naz
- Muhammad Nawaz Sharif university of engineering and technology, Multan, Pakistan
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Ustebay S, Sarmis A, Kaya GK, Sujan M. A comparison of machine learning algorithms in predicting COVID-19 prognostics. Intern Emerg Med 2023; 18:229-239. [PMID: 36116079 PMCID: PMC9483274 DOI: 10.1007/s11739-022-03101-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Accepted: 09/05/2022] [Indexed: 02/01/2023]
Abstract
ML algorithms are used to develop prognostic and diagnostic models and so to support clinical decision-making. This study uses eight supervised ML algorithms to predict the need for intensive care, intubation, and mortality risk for COVID-19 patients. The study uses two datasets: (1) patient demographics and clinical data (n = 11,712), and (2) patient demographics, clinical data, and blood test results (n = 602) for developing the prediction models, understanding the most significant features, and comparing the performances of eight different ML algorithms. Experimental findings showed that all prognostic prediction models reported an AUROC value of over 0.92, in which extra tree and CatBoost classifiers were often outperformed (AUROC over 0.94). The findings revealed that the features of C-reactive protein, the ratio of lymphocytes, lactic acid, and serum calcium have a substantial impact on COVID-19 prognostic predictions. This study provides evidence of the value of tree-based supervised ML algorithms for predicting prognosis in health care.
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Affiliation(s)
- Serpil Ustebay
- Department of Computer Engineering, Istanbul Medeniyet University, Istanbul, Turkey
| | - Abdurrahman Sarmis
- Department of Microbiology Laboratory, Goztepe Prof. Dr. Suleyman Yalcin City Hospital, Istanbul, Turkey
| | - Gulsum Kubra Kaya
- Department of Industrial Engineering, Istanbul Medeniyet University, Istanbul, Turkey.
- School of Aerospace, Transport and Manufacturing, Cranfield University, Bedford, MK430AL, UK.
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36
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Prediction of the COVID-19 infectivity and the sustainable impact on public health under deep learning algorithm. Soft comput 2023; 27:2695-2704. [PMID: 34456617 PMCID: PMC8380005 DOI: 10.1007/s00500-021-06142-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/10/2021] [Indexed: 10/24/2022]
Abstract
The aim is to explore the development trend of COVID-19 (Corona Virus Disease 2019) and predict the infectivity of 2019-nCoV (2019 Novel Coronavirus), as well as its impact on public health. First, the existing data are analyzed through data pre-processing to extract useful feature factors. Then, the LSTM (Long-Short Term Memory) prediction model in the deep learning algorithm is used to predict the epidemic situation in Hubei Province, outside Hubei nationwide, and the whole country, respectively. Meanwhile, the impact of intervention time changes on the epidemic situation is compared. The results show that the prediction results are almost consistent with the actual values. Specifically, Hubei Province abolishes quarantine restrictions after the Spring Festival holiday, and the first COVID-19 peak is reached in late February, while the second COVID-19 peak has been reached in early March. Finally, the cumulative number of diagnoses reaches 85,000 cases, with an increase of 15,000 cases compared with the nationwide cases outside Hubei under the continuous implementation of prevention and control measures. Under the prediction of the proposed LSTM model, if the nationwide implementation of prevention and control interventions is postponed by 5 days, the epidemic will peak in early March, and the cumulative number of diagnoses will be about 200,000; and if the intervention measures are implemented five days earlier, the epidemic will peak in mid-February, with a cumulative number of diagnoses of approximately 40,000. Meanwhile, the proposed LSTM model predicts the RMSE values of the epidemic situation in Hubei Province, outside Hubei nationwide, and the whole country as 34.63, 75.42, and 50.27, respectively. Under model comparison analysis, the prediction error of the proposed LSTM model is small and has better applicability over similar algorithms. The results show that the LSTM model is effective and has high performance in infectious disease prediction, and the research results can provide scientific and effective references for subsequent related research.
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Alsaeedi MAK, Kurnaz S. RETRACTED ARTICLE: Feature selection for diagnose coronavirus (COVID-19) disease by neural network and Caledonian crow learning algorithm. APPLIED NANOSCIENCE 2023; 13:3129. [PMID: 35155058 PMCID: PMC8818372 DOI: 10.1007/s13204-021-02159-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 10/09/2021] [Indexed: 11/07/2022]
Affiliation(s)
| | - Sefer Kurnaz
- Department of Electrical Computer Engineering, Altinbas University, Istanbul, Turkey
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38
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Marefat A, Marefat M, Hassannataj Joloudari J, Nematollahi MA, Lashgari R. CCTCOVID: COVID-19 detection from chest X-ray images using Compact Convolutional Transformers. Front Public Health 2023; 11:1025746. [PMID: 36923036 PMCID: PMC10009152 DOI: 10.3389/fpubh.2023.1025746] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Accepted: 02/07/2023] [Indexed: 03/03/2023] Open
Abstract
COVID-19 is a novel virus that attacks the upper respiratory tract and the lungs. Its person-to-person transmissibility is considerably rapid and this has caused serious problems in approximately every facet of individuals' lives. While some infected individuals may remain completely asymptomatic, others have been frequently witnessed to have mild to severe symptoms. In addition to this, thousands of death cases around the globe indicated that detecting COVID-19 is an urgent demand in the communities. Practically, this is prominently done with the help of screening medical images such as Computed Tomography (CT) and X-ray images. However, the cumbersome clinical procedures and a large number of daily cases have imposed great challenges on medical practitioners. Deep Learning-based approaches have demonstrated a profound potential in a wide range of medical tasks. As a result, we introduce a transformer-based method for automatically detecting COVID-19 from X-ray images using Compact Convolutional Transformers (CCT). Our extensive experiments prove the efficacy of the proposed method with an accuracy of 99.22% which outperforms the previous works.
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Affiliation(s)
- Abdolreza Marefat
- Department of Computer Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran
| | - Mahdieh Marefat
- Department of Cellular and Molecular Biology, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | | | | | - Reza Lashgari
- Institute of Medical Science and Technology, Shahid Beheshti University, Tehran, Iran
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Bartenschlager CC, Ebel SS, Kling S, Vehreschild J, Zabel LT, Spinner CD, Schuler A, Heller AR, Borgmann S, Hoffmann R, Rieg S, Messmann H, Hower M, Brunner JO, Hanses F, Römmele C. COVIDAL: A machine learning classifier for digital COVID-19 diagnosis in German hospitals. ACM TRANSACTIONS ON MANAGEMENT INFORMATION SYSTEMS 2022. [DOI: 10.1145/3567431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
For the fight against the COVID-19 pandemic, it is particularly important to map the course of infection, in terms of patients who have currently tested SARS-CoV-2 positive, as accurately as possible. In hospitals, this is even more important because resources have become scarce. Although polymerase chain reaction (PCR) and point of care (POC) antigen testing capacities have been massively expanded, they are often very time-consuming and cost-intensive and, in some cases, lack appropriate performance. To meet these challenges, we propose the COVIDAL classifier for AI-based diagnosis of symptomatic COVID-19 subjects in hospitals based on laboratory parameters. We evaluate the algorithm's performance by unique multicenter data with approx. 4,000 patients and an extraordinary high ratio of SARS-CoV-2 positive patients. We analyze the influence of data preparation, flexibility in optimization targets as well as the selection of the test set on the COVIDAL outcome. The algorithm is compared with standard AI, PCR, POC antigen testing and manual classifications of seven physicians by a decision theoretic scoring model including performance metrics, turnaround times and cost. Thereby, we define health care settings in which a certain classifier for COVID-19 diagnosis is to be applied. We find sensitivities, specificities and accuracies of the COVIDAL algorithm of up to 90 percent. Our scoring model suggests using PCR testing for a focus on performance metrics. For turnaround times, POC antigen testing should be used. If balancing performance, turnaround times and cost is of interest, as, for example, in the emergency department, COVIDAL is superior based on the scoring model.
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Affiliation(s)
- Christina C. Bartenschlager
- Chair of Health Care Operations/Health Information Management, Faculty of Business and Economics, Faculty of Medicine, University of Augsburg, Universitätsstraße 16, 86159 Augsburg, Germany
| | - Stefanie S. Ebel
- Chair of Health Care Operations/Health Information Management, Faculty of Business and Economics, Faculty of Medicine, University of Augsburg, Universitätsstraße 16, 86159 Augsburg, Germany
| | - Sebastian Kling
- Chair of Health Care Operations/Health Information Management, Faculty of Business and Economics, Faculty of Medicine, University of Augsburg, Universitätsstraße 16, 86159 Augsburg, Germany
| | - Janne Vehreschild
- Department II of Internal Medicine, Hematology/Oncology, Goethe University, Frankfurt, Frankfurt am Main, Germany; Department I of Internal Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany; German Centre for Infection Research (DZIF), partner site Bonn-Cologne, Cologne, Germany
| | - Lutz T. Zabel
- Laboratory Medicine, Alb Fils Kliniken GmbH, Eichertstraße 3, 73035 Göppingen, Germany
| | - Christoph D. Spinner
- Technical University of Munich, School of Medicine, University Hospital rechts der Isar, Department of Internal Medicine II, Ismaninger Str. 22, 81675 Munich, Germany
| | - Andreas Schuler
- Gastroenterology, Alb Fils Kliniken GmbH, Eichertstraße 3, 73035 Göppingen, Germany
| | - Axel R. Heller
- Anaesthesiology and Operative Intensive Care Medicine, Medical Faculty, University of Augsburg, Stenglinstrasse 2, 86156 Augsburg, Germany
| | | | - Reinhard Hoffmann
- Laboratory Medicine and Microbiology, Medical Faculty, University of Augsburg, Stenglinstrasse 2, 86156 Augsburg, Germany
| | - Siegbert Rieg
- Clinic for Internal Medicine II - Infectiology, University Hospital Freiburg, Germany
| | - Helmut Messmann
- Clinic for Internal Medicine III - Gastroenterology and Infectious Diseases, University Hospital Augsburg, Stenglinstraße 2, 86156 Augsburg, Germany
| | - Martin Hower
- Department of Pneumology, Infectious Diseases and Intensive Care, Klinikum Dortmund gGmbH, Hospital of University Witten / Herdecke, 44137 Dortmund, Germany
| | - Jens O. Brunner
- Chair of Health Care Operations/Health Information Management, Faculty of Business and Economics, Faculty of Medicine, University of Augsburg, Universitätsstraße 16, 86159 Augsburg, Germany
| | - Frank Hanses
- Emergency Department, University Hospital Regensburg, Germany; Department for Infection Control and Infectious Diseases, University Hospital Regensburg, Germany
| | - Christoph Römmele
- Clinic for Internal Medicine III - Gastroenterology and Infectious Diseases, University Hospital Augsburg, Stenglinstraße 2, 86156 Augsburg, Germany
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40
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Hasija S, Akash P, Bhargav Hemanth M, Kumar A, Sharma S. A novel approach for detection of COVID-19 and Pneumonia using only binary classification from chest CT-scans. NEUROSCIENCE INFORMATICS 2022; 2:100069. [PMID: 36741276 PMCID: PMC8958781 DOI: 10.1016/j.neuri.2022.100069] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 03/22/2022] [Indexed: 02/07/2023]
Abstract
The novel Coronavirus, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) spread all over the world, causing a dramatic shift in circumstances that resulted in a massive pandemic, affecting the world's well-being and stability. It is an RNA virus that can infect both humans as well as animals. Diagnosis of the virus as soon as possible could contain and avoid a serious COVID-19 outbreak. Current pharmaceutical techniques and diagnostic methods tests such as Reverse Transcription-Polymerase Chain Reaction (RT-PCR) and Serology tests are time-consuming, expensive, and require a well-equipped laboratory for analysis, making them restrictive and inaccessible to everyone. Deep Learning has grown in popularity in recent years, and it now plays a crucial role in Image Classification, which also involves Medical Imaging. Using chest CT scans, this study explores the problem statement automation of differentiating COVID-19 contaminated individuals from healthy individuals. Convolutional Neural Networks (CNNs) can be trained to detect patterns in computed tomography scans (CT scans). Hence, different CNN models were used in the current study to identify variations in chest CT scans, with accuracies ranging from 91% to 98%. The Multiclass Classification method is used to build these architectures. This study also proposes a new approach for classifying CT images that use two binary classifications combined to work together, achieving 98.38% accuracy. All of these architectures' performances are compared using different classification metrics.
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41
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Yenurkar G, Mal S. Future forecasting prediction of Covid-19 using hybrid deep learning algorithm. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 82:22497-22523. [PMID: 36415331 PMCID: PMC9672606 DOI: 10.1007/s11042-022-14219-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 10/27/2022] [Accepted: 11/01/2022] [Indexed: 06/01/2023]
Abstract
Due the quick spread of coronavirus disease 2019 (COVID-19), identification of that disease, prediction of mortality rate and recovery rate are considered as one of the critical challenges in the whole world. The occurrence of COVID-19 dissemination beyond the world is analyzed in this research and an artificial-intelligence (AI) based deep learning algorithm is suggested to detect positive cases of COVID19 patients, mortality rate and recovery rate using real-world datasets. Initially, the unwanted data like prepositions, links, hashtags etc., are removed using some pre-processing techniques. After that, term frequency inverse-term frequency (TF-IDF) andBag of Words (BoW) techniques are utilized to extract the features from pre-processed dataset. Then, Mayfly Optimization (MO) algorithm is performed to pick the relevant features from the set of features. Finally, two deep learning procedures, ResNet model and GoogleNet model, are hybridized to achieve the prediction process. Our system examines two different kinds of publicly available text datasets to identify COVID-19 disease as well as to predict mortality rate and recovery rate using those datasets. There are four different datasets are taken to analyse the performance, in which the proposed method achieves 97.56% accuracy which is 1.40% greater than Linear Regression (LR) and Multinomial Naive Bayesian (MNB), 3.39% higher than Random Forest (RF) and Stochastic gradient boosting (SGB) as well as 5.32% higher than Decision tree (DT) and Bagging techniques if first dataset. When compared to existing machine learning models, the simulation result indicates that a proposed hybrid deep learning method is valuable in corona virus identification and future mortality forecast study.
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Affiliation(s)
- Ganesh Yenurkar
- School of Computing Science & Engineering, VIT Bhopal University, Bhopal, India
- Yeshwantrao Chavan College of Engineering, Wanadongri, Nagpur, India
| | - Sandip Mal
- School of Computing Science & Engineering, VIT Bhopal University, Bhopal, India
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42
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Das B. An implementation of a hybrid method based on machine learning to identify biomarkers in the Covid-19 diagnosis using DNA sequences. CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS : AN INTERNATIONAL JOURNAL SPONSORED BY THE CHEMOMETRICS SOCIETY 2022; 230:104680. [PMID: 36213553 PMCID: PMC9528020 DOI: 10.1016/j.chemolab.2022.104680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 09/20/2022] [Accepted: 09/27/2022] [Indexed: 06/16/2023]
Abstract
Although some people do not have any chronic disease or are not in the risky age group for Covid-19, they are more vulnerable to the coronavirus. As the reason for this situation, some experts focus on the immune system of the person, while others think that the genetic history of patients may play a role. It is critical to detect corona from DNA signals as early as possible to determine the relationship between Covid-19 and genes. Thus, the effect on the severe course of the disease of variations in the genes associated with the corona disease will be revealed. In this study, a novel intelligent computer approach is proposed to identify coronavirus from nucleotide signals for the first time. The proposed method presents a multilayered feature extraction structure to extract the most effective features using an Entropy-based mapping technique, Discrete Wavelet Transform (DWT), statistical feature extractor, and Singular Value Decomposition (SVD), together. Then 94 distinctive features are selected by the ReliefF technique. Support vector machine (SVM) and k nearest neighborhood (k-NN) are chosen as classifiers. The method achieved the highest classification accuracy rate of 98.84% with an SVM classifier to detect Covid-19 from DNA signals. The proposed method is ready to be tested with a different database in the diagnosis of Covid-19 using RNA or other signals.
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Affiliation(s)
- Bihter Das
- Department of Software Engineering, Technology Faculty, Firat University, 23119, Elazig, Turkey
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43
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Er MB. COVID-19 detection based on pre-trained deep networks and LSTM model using X-ray images enhanced contrast with artificial bee colony algorithm. EXPERT SYSTEMS 2022; 40:e13185. [PMID: 36718212 PMCID: PMC9878115 DOI: 10.1111/exsy.13185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 09/14/2022] [Accepted: 10/20/2022] [Indexed: 06/18/2023]
Abstract
Coronavirus (COVID-19) is an infectious disease that has spread across the world within a short period of time and is causing rapid casualties. The main symptoms of this virus are shortness of breath, fever, cough, and a sore throat. The virus is detected through samples, such as throat swabs and sputum, taken from people who meet the possible case definition and the results are usually obtained within a few hours or a day. The development of test kits to detect the COVID-19 virus is still an open research topic, and automated and faster diagnostic tools are needed. Recent studies have shown that biomedical images can be used for COVID-19 testing. This study proposes the hybrid use of pre-trained deep networks and the long short-term memory (LSTM) for the classification of COVID-19 from contrast-enhanced chest X-rays. In the proposed system, a transformation function is applied to X-ray images first. Then, the artificial bee colony (ABC) algorithm is used to optimize the parameters obtained from the transformation function. The pre-trained deep network models and LSTM are preferred to extract features from the contrast-enhanced chest X-rays. At the final stage, COVID-19, normal (healthy), and pneumonia chest X-ray are classified using softmax. To evaluate the performance of the proposed method, the "COVID-19 radiography" dataset, which is widely used in the literature, is preferred. From the proposed model, 98.97% accuracy, 98.80% precision, and 98.70% sensitivity rates are obtained. Experimental results reveal that the proposed model provides efficient results compared to other methods. Thanks to the application of ABC-based image enhancement, increased classification of 2.5% has been achieved against other state-of-the-art models.
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Affiliation(s)
- Mehmet Bilal Er
- Department of Computer Engineering, Faculty of EngineeringHarran UniversityŞanlıurfaTurkey
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44
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Shibu George G, Raj Mishra P, Sinha P, Ranjan Prusty M. COVID-19 Detection on Chest X-Ray Images Using Homomorphic Transformation and VGG Inspired Deep Convolutional Neural Network. Biocybern Biomed Eng 2022; 43:1-16. [PMID: 36447948 PMCID: PMC9684127 DOI: 10.1016/j.bbe.2022.11.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 11/01/2022] [Accepted: 11/18/2022] [Indexed: 11/25/2022]
Abstract
COVID-19 had caused the whole world to come to a standstill. The current detection methods are time consuming as well as costly. Using Chest X-rays (CXRs) is a solution to this problem, however, manual examination of CXRs is a cumbersome and difficult process needing specialization in the domain. Most of existing methods used for this application involve the usage of pretrained models such as VGG19, ResNet, DenseNet, Xception, and EfficeintNet which were trained on RGB image datasets. X-rays are fundamentally single channel images, hence using RGB trained model is not appropriate since it increases the operations by involving three channels instead of one. A way of using pretrained model for grayscale images is by replicating the one channel image data to three channel which introduces redundancy and another way is by altering the input layer of pretrained model to take in one channel image data, which comprises the weights in the forward layers that were trained on three channel images which weakens the use of pre-trained weights in a transfer learning approach. A novel approach for identification of COVID-19 using CXRs, Contrast Limited Adaptive Histogram Equalization (CLAHE) along with Homomorphic Transformation Filter which is used to process the pixel data in images and extract features from the CXRs is suggested in this paper. These processed images are then provided as input to a VGG inspired deep Convolutional Neural Network (CNN) model which takes one channel image data as input (grayscale images) to categorize CXRs into three class labels, namely, No-Findings, COVID-19, and Pneumonia. Evaluation of the suggested model is done with the help of two publicly available datasets; one to obtain COVID-19 and No-Finding images and the other to obtain Pneumonia CXRs. The dataset comprises 6750 images in total; 2250 images for each class. Results obtained show that the model has achieved 96.56% for multi-class classification and 98.06% accuracy for binary classification using 5-fold stratified cross validation (CV) method. This result is competitive and up to the mark when compared with the performance shown by existing approaches for COVID-19 classification.
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Affiliation(s)
- Gerosh Shibu George
- School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu 600127, India
| | - Pratyush Raj Mishra
- School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu 600127, India
| | - Panav Sinha
- School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu 600127, India
| | - Manas Ranjan Prusty
- Centre for Cyber Physical Systems, School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu 600127, India
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45
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Saheb SK, Narayanan B, Rao TVN. ADL-CDF: A Deep Learning Framework for COVID-19 Detection from CT Scans Towards an Automated Clinical Decision Support System. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2022; 48:1-13. [PMID: 36212631 PMCID: PMC9531859 DOI: 10.1007/s13369-022-07271-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 09/06/2022] [Indexed: 11/29/2022]
Abstract
The emergence of deep learning has paved to solve many problems in the real world. COVID-19 pandemic, since the late 2019, has been affecting lives of people across the globe. Chest CT scan images are used to detect it and know its severity in patients. The problem with many existing solutions in COVID-19 detection using CT scan images is that inability to detect the infection when it is in initial stages. As the infection can exist on varied scales, there is need for more comprehensive approach that can ascertain the disease at all scales. Towards this end, we proposed a deep learning-based framework known as Automated Deep Learning-based COVID-19 Detection Framework (ADL-CDF). It does not need a human medical expert in diagnosis as it is capable of detecting automatically. The framework is assisted by two algorithms that involve image processing and deep learning. The first algorithm known as Region of Interest (ROI)-based Image Filtering (ROI-IF) which analyses given input CT scan images of a patient and discards the ones where ROI is missing. This algorithm minimizes time taken for processing besides reducing false positive rate. The second algorithm is known as Multi-Scale Feature Selection algorithm that fits into the deep learning framework's pipeline to leverage detection performance of the ADL-CDF. The proposed framework is evaluated against ResNet50V2 and Xception. Our empirical study revealed that our model outperforms the state of the art.
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Affiliation(s)
- Shaik Khasim Saheb
- Department of CSE, Annamalai University, Annamalainagar, Chidambaram, Tamil Nadu India
- Department of Computer Science and Engineering, Sreenidhi Institute of Science and Technology, Yamnampet, Ghatkesar, Hyderabad, Telangana India
| | - B. Narayanan
- Department of CSE, Annamalai University, Annamalainagar, Chidambaram, Tamil Nadu India
| | - Thota Venkat Narayana Rao
- Department of Computer Science and Engineering, Sreenidhi Institute of Science and Technology, Yamnampet, Ghatkesar, Hyderabad, Telangana India
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46
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Karthik R, Menaka R, Hariharan M, Kathiresan GS. AI for COVID-19 Detection from Radiographs: Incisive Analysis of State of the Art Techniques, Key Challenges and Future Directions. Ing Rech Biomed 2022; 43:486-510. [PMID: 34336141 PMCID: PMC8312058 DOI: 10.1016/j.irbm.2021.07.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 06/14/2021] [Accepted: 07/19/2021] [Indexed: 12/24/2022]
Abstract
Background and objective In recent years, Artificial Intelligence has had an evident impact on the way research addresses challenges in different domains. It has proven to be a huge asset, especially in the medical field, allowing for time-efficient and reliable solutions. This research aims to spotlight the impact of deep learning and machine learning models in the detection of COVID-19 from medical images. This is achieved by conducting a review of the state-of-the-art approaches proposed by the recent works in this field. Methods The main focus of this study is the recent developments of classification and segmentation approaches to image-based COVID-19 detection. The study reviews 140 research papers published in different academic research databases. These papers have been screened and filtered based on specified criteria, to acquire insights prudent to image-based COVID-19 detection. Results The methods discussed in this review include different types of imaging modality, predominantly X-rays and CT scans. These modalities are used for classification and segmentation tasks as well. This review seeks to categorize and discuss the different deep learning and machine learning architectures employed for these tasks, based on the imaging modality utilized. It also hints at other possible deep learning and machine learning architectures that can be proposed for better results towards COVID-19 detection. Along with that, a detailed overview of the emerging trends and breakthroughs in Artificial Intelligence-based COVID-19 detection has been discussed as well. Conclusion This work concludes by stipulating the technical and non-technical challenges faced by researchers and illustrates the advantages of image-based COVID-19 detection with Artificial Intelligence techniques.
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Affiliation(s)
- R Karthik
- Centre for Cyber Physical Systems, Vellore Institute of Technology, Chennai, India
| | - R Menaka
- Centre for Cyber Physical Systems, Vellore Institute of Technology, Chennai, India
| | - M Hariharan
- School of Computing Sciences and Engineering, Vellore Institute of Technology, Chennai, India
| | - G S Kathiresan
- School of Electronics Engineering, Vellore Institute of Technology, Chennai, India
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Alsaaidah B, Al-Hadidi MR, Al-Nsour H, Masadeh R, AlZubi N. Comprehensive Survey of Machine Learning Systems for COVID-19 Detection. J Imaging 2022; 8:267. [PMID: 36286361 PMCID: PMC9604704 DOI: 10.3390/jimaging8100267] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 09/11/2022] [Accepted: 09/20/2022] [Indexed: 01/14/2023] Open
Abstract
The last two years are considered the most crucial and critical period of the COVID-19 pandemic affecting most life aspects worldwide. This virus spreads quickly within a short period, increasing the fatality rate associated with the virus. From a clinical perspective, several diagnosis methods are carried out for early detection to avoid virus propagation. However, the capabilities of these methods are limited and have various associated challenges. Consequently, many studies have been performed for COVID-19 automated detection without involving manual intervention and allowing an accurate and fast decision. As is the case with other diseases and medical issues, Artificial Intelligence (AI) provides the medical community with potential technical solutions that help doctors and radiologists diagnose based on chest images. In this paper, a comprehensive review of the mentioned AI-based detection solution proposals is conducted. More than 200 papers are reviewed and analyzed, and 145 articles have been extensively examined to specify the proposed AI mechanisms with chest medical images. A comprehensive examination of the associated advantages and shortcomings is illustrated and summarized. Several findings are concluded as a result of a deep analysis of all the previous works using machine learning for COVID-19 detection, segmentation, and classification.
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Affiliation(s)
- Bayan Alsaaidah
- Department of Computer Science, Prince Abdullah bin Ghazi Faculty of Information Technology and Communications, Al-Balqa Applied University, Salt 19117, Jordan
| | - Moh’d Rasoul Al-Hadidi
- Department of Electrical Engineering, Electrical Power Engineering and Computer Engineering, Faculty of Engineering, Al-Balqa Applied University, Salt 19117, Jordan
| | - Heba Al-Nsour
- Department of Computer Science, Prince Abdullah bin Ghazi Faculty of Information Technology and Communications, Al-Balqa Applied University, Salt 19117, Jordan
| | - Raja Masadeh
- Computer Science Department, The World Islamic Sciences and Education University, Amman 11947, Jordan
| | - Nael AlZubi
- Department of Electrical Engineering, Electrical Power Engineering and Computer Engineering, Faculty of Engineering, Al-Balqa Applied University, Salt 19117, Jordan
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48
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Pradhan AK, Mishra D, Das K, Obaidat MS, Kumar M. A COVID-19 X-ray image classification model based on an enhanced convolutional neural network and hill climbing algorithms. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 82:14219-14237. [PMID: 36185320 PMCID: PMC9513301 DOI: 10.1007/s11042-022-13826-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 03/17/2022] [Accepted: 09/06/2022] [Indexed: 06/16/2023]
Abstract
The classification of medical images is significant among researchers and physicians for the early identification and clinical treatment of many disorders. Though, traditional classifiers require more time and effort for feature extraction and reduction from images. To overcome this problem, there is a need for a new deep learning method known as Convolution Neural Network (CNN), which shows the high performance and self-learning capabilities. In this paper,to classify whether a chest X-ray (CXR) image shows pneumonia (Normal) or COVID-19 illness, a test-bed analysis has been carried out between pre-trained CNN models like Visual Geometry Group (VGG-16), VGG-19, Inception version 3 (INV3), Caps Net, DenseNet121, Residual Neural Network with 50 deep layers (ResNet50), Mobile-Net and proposed CNN classifier. It has been observed that, in terms of accuracy, the proposed CNN model appears to be potentially superior to others. Additionally, in order to increase the performance of the CNN classifier, a nature-inspired optimization method known as Hill-Climbing Algorithm based CNN (CNN-HCA) model has been proposed to enhance the CNN model's parameters. The proposed CNN-HCA model performance is tested using a simulation study and contrasted to existing hybridized classifiers like as Particle Swarm Optimization (CNN-PSO) and CNN-Jaya. The proposed CNN-HCA model is compared with peer reviewed works in the same domain. The CXR dataset, which is freely available on the Kaggle repository, was used for all experimental validations. In terms of Receiver Operating Characteristic Curve (ROC), Area Under the ROC Curve (AUC), sensitivity, specificity, F-score, and accuracy, the simulation findings show that the CNN-HCA is possibly superior than existing hybrid approaches. Each method employs a k-fold stratified cross-validation strategy to reduce over-fitting.
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Affiliation(s)
- Ashwini Kumar Pradhan
- Department of Computer Science and Engineering, Siksha O Anusandhan (Deemed to Be University), Khandagiri, Bhubaneswar, 751030 Odisha India
| | - Debahuti Mishra
- Department of Computer Science and Engineering, Siksha O Anusandhan (Deemed to Be University), Khandagiri, Bhubaneswar, 751030 Odisha India
| | - Kaberi Das
- Department of Computer Science and Engineering, Siksha O Anusandhan (Deemed to Be University), Khandagiri, Bhubaneswar, 751030 Odisha India
| | - Mohammad S. Obaidat
- Distingsuhed Professor, Indian Institute of Technology, Dhanbad, India
- KASIT, University of Jordan, Amman, Jordan
- University of Science and Technology Beijing, Beijing, China
| | - Manoj Kumar
- Faculty of Engineering and Information Sciences, University of Wollongong in Dubai, Dubai Knowledge Park, Dubai, United Arab Emirates
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Bhosale YH, Patnaik KS. Application of Deep Learning Techniques in Diagnosis of Covid-19 (Coronavirus): A Systematic Review. Neural Process Lett 2022; 55:1-53. [PMID: 36158520 PMCID: PMC9483290 DOI: 10.1007/s11063-022-11023-0] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/29/2022] [Indexed: 01/09/2023]
Abstract
Covid-19 is now one of the most incredibly intense and severe illnesses of the twentieth century. Covid-19 has already endangered the lives of millions of people worldwide due to its acute pulmonary effects. Image-based diagnostic techniques like X-ray, CT, and ultrasound are commonly employed to get a quick and reliable clinical condition. Covid-19 identification out of such clinical scans is exceedingly time-consuming, labor-intensive, and susceptible to silly intervention. As a result, radiography imaging approaches using Deep Learning (DL) are consistently employed to achieve great results. Various artificial intelligence-based systems have been developed for the early prediction of coronavirus using radiography pictures. Specific DL methods such as CNN and RNN noticeably extract extremely critical characteristics, primarily in diagnostic imaging. Recent coronavirus studies have used these techniques to utilize radiography image scans significantly. The disease, as well as the present pandemic, was studied using public and private data. A total of 64 pre-trained and custom DL models concerning imaging modality as taxonomies are selected from the studied articles. The constraints relevant to DL-based techniques are the sample selection, network architecture, training with minimal annotated database, and security issues. This includes evaluating causal agents, pathophysiology, immunological reactions, and epidemiological illness. DL-based Covid-19 detection systems are the key focus of this review article. Covid-19 work is intended to be accelerated as a result of this study.
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Affiliation(s)
- Yogesh H. Bhosale
- Department of Computer Science and Engineering, Birla Institute of Technology, Mesra, Ranchi 835215 India
| | - K. Sridhar Patnaik
- Department of Computer Science and Engineering, Birla Institute of Technology, Mesra, Ranchi 835215 India
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50
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Alakus TB, Turkoglu I. Prediction of viral-host interactions of COVID-19 by computational methods. CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS : AN INTERNATIONAL JOURNAL SPONSORED BY THE CHEMOMETRICS SOCIETY 2022; 228:104622. [PMID: 35879939 PMCID: PMC9301933 DOI: 10.1016/j.chemolab.2022.104622] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 06/20/2022] [Accepted: 07/10/2022] [Indexed: 06/15/2023]
Abstract
Experimental approaches are currently used to determine viral-host interactions, but these approaches are both time-consuming and costly. For these reasons, computational-based approaches are recommended. In this study, using computational-based approaches, viral-host interactions of SARS-CoV-2 virus and human proteins were predicted. The study consists of four different stages; in the first stage viral and host protein sequences were obtained. In the second stage, protein sequences were converted into numerical expressions by various protein mapping methods. These methods are entropy-based, AVL-tree, FIBHASH, binary encoding, CPNR, PAM250, BLOSUM62, Atchley factors, Meiler parameters, EIIP, AESNN1, Miyazawa energies, Micheletti potentials, Z-scale, and hydrophobicity. In the third stage, a deep learning model was designed and BiLSTM was used for this. In the last stage, the protein sequences were classified, and the viral-host interactions were predicted. The performances of protein mapping methods were determined by accuracy, F1-score, specificity, sensitivity, and AUC scores. According to the classification results, the best classification process was obtained by the entropy-based method. With this method, 94.74% accuracy, and 0.95 AUC score were calculated. Then, the most successful classification process was performed with the Z-scale and 91.23% accuracy, and 0.96 AUC score were obtained. Although other protein mapping methods are not as efficient as Z-scale and entropy-based methods, they have achieved successful classification. AVL-tree, FIBHASH, binary encoding, CPNR, PAM250, BLOSUM62, Atchley factors, Meiler parameters and AESNN1 methods showed over 80% accuracy, F1-score, and AUC score. Accuracy scores of EIIP, Miyazawa energies, Micheletti potentials and hydrophobicity methods remained below 80%. When the results were examined in general, it was observed that the computational approaches were successful in predicting viral-host interactions between SARS-CoV-2 virus and human proteins.
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Affiliation(s)
- Talha Burak Alakus
- Kirklareli University, Department of Software Engineering, Kirklareli, 39000, Turkey
| | - Ibrahim Turkoglu
- Firat University, Department of Software Engineering, Elazig, 23119, Turkey
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