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gharehhasani BS, Rezaei M, Naghipour A, Sayad N, Mostafaei S, Alimohammadi E. The most important variables associated with death due to COVID-19 disease, based on three data mining models Decision Tree, AdaBoost, and Support Vector Machine: A cross-sectional study. Health Sci Rep 2024; 7:e2266. [PMID: 39055612 PMCID: PMC11269761 DOI: 10.1002/hsr2.2266] [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: 10/11/2023] [Revised: 06/06/2024] [Accepted: 07/10/2024] [Indexed: 07/27/2024] Open
Abstract
Introduction Death due to covid-19 is one of the biggest health challenges in the world. There are many models that can predict death due to COVID-19. This study aimed to fit and compare Decision Tree (DT), Support Vector Machine (SVM), and AdaBoost models to predict death due to COVID-19. Methods To describe the variables, mean (SD) and frequency (%) were reported. To determine the relationship between the variables and the death caused by COVID-19, chi-square test was performed with a significance level of 0.05. To compare DT, SVM and AdaBoost models for predicting death due to COVID-19 from sensitivity, specificity, accuracy and the area under the rock curve under R software using psych, caTools, random over-sampling examples, rpart, rpartplot packages was done. Results Out of the total of 23,054 patients studied, 10,935 cases (46.5%) were women, and 12,569 cases (53.5%) were men. Additionally, the mean age of the patients was 54.9 ± 21.0 years. There is a statistically significant relationship between gender, fever, cough, muscle pain, smell and taste, abdominal pain, nausea and vomiting, diarrhea, anorexia, dizziness, chest pain, intubation, cancer, diabetes, chronic blood disease, Violation of immunity, pregnancy, Dialysis, chronic lung disease with the death of covid-19 patients showed (p < 0.05). The results showed that the sensitivity, specificity, accuracy and the area under the receiver operating characteristic curve were respectively 0.60, 0.68, 0.71, and 0.75 in the DT model, 0.54, 0.62, 0.63, and 0.71 in the SVM model, and 0.59, 0.65, 0.69 and 0.74 in the AdaBoost model. Conclusion The results showed that DT had a high predictive power compared to other data mining models. Therefore, it is suggested to researchers in different fields to use DT to predict the studied variables. Also, it is suggested to use other approaches such as random forest or XGBoost to improve the accuracy in future studies.
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Affiliation(s)
| | - Mansour Rezaei
- Social Development and Health Promotion Research CenterKermanshah University of Medical SciencesKermanshahIran
| | - Armin Naghipour
- Clinical Research Development Center, Imam Khomeini and Dr.Mohammad Kermanshahi and Farabi HospitalsKermanshah University of Medical SciencesKermanshahIran
| | | | - Shayan Mostafaei
- Department of Medical Epidemiology and BiostatisticsKarolinska InstituteStockholmSweden
| | - Ehsan Alimohammadi
- Department of NeurosurgeryKermanshah University of Medical SciencesKermanshahIran
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Asci E, Kilic M, Celik O, Cantekin K, Bircan HB, Bayrakdar İS, Orhan K. A Deep Learning Approach to Automatic Tooth Caries Segmentation in Panoramic Radiographs of Children in Primary Dentition, Mixed Dentition, and Permanent Dentition. CHILDREN (BASEL, SWITZERLAND) 2024; 11:690. [PMID: 38929269 PMCID: PMC11202197 DOI: 10.3390/children11060690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Revised: 05/03/2024] [Accepted: 05/20/2024] [Indexed: 06/28/2024]
Abstract
OBJECTIVES The purpose of this study was to evaluate the effectiveness of dental caries segmentation on the panoramic radiographs taken from children in primary dentition, mixed dentition, and permanent dentition with Artificial Intelligence (AI) models developed using the deep learning method. METHODS This study used 6075 panoramic radiographs taken from children aged between 4 and 14 to develop the AI model. The radiographs included in the study were divided into three groups: primary dentition (n: 1857), mixed dentition (n: 1406), and permanent dentition (n: 2812). The U-Net model implemented with PyTorch library was used for the segmentation of caries lesions. A confusion matrix was used to evaluate model performance. RESULTS In the primary dentition group, the sensitivity, precision, and F1 scores calculated using the confusion matrix were found to be 0.8525, 0.9128, and 0.8816, respectively. In the mixed dentition group, the sensitivity, precision, and F1 scores calculated using the confusion matrix were found to be 0.7377, 0.9192, and 0.8185, respectively. In the permanent dentition group, the sensitivity, precision, and F1 scores calculated using the confusion matrix were found to be 0.8271, 0.9125, and 0.8677, respectively. In the total group including primary, mixed, and permanent dentition, the sensitivity, precision, and F1 scores calculated using the confusion matrix were 0.8269, 0.9123, and 0.8675, respectively. CONCLUSIONS Deep learning-based AI models are promising tools for the detection and diagnosis of caries in panoramic radiographs taken from children with different dentition.
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Affiliation(s)
- Esra Asci
- Department of Pediatric Dentistry, Faculty of Dentistry, Ataturk University, Erzurum 25240, Turkey; (E.A.); (H.B.B.)
| | - Munevver Kilic
- Department of Pediatric Dentistry, Faculty of Dentistry, Beykent University, İstanbul 34398, Turkey
| | - Ozer Celik
- Department of Mathematics Computer, Faculty of Science, Eskisehir Osmangazi University, Eskisehir 26040, Turkey;
- Center of Research and Application for Computer Aided Diagnosis and Treatment in Health, Eskisehir Osmangazi University, Eskisehir 26040, Turkey;
| | - Kenan Cantekin
- Department of Pediatric Dentistry, Faculty of Dentistry, Sakarya University, Sakarya 54050, Turkey;
| | - Hasan Basri Bircan
- Department of Pediatric Dentistry, Faculty of Dentistry, Ataturk University, Erzurum 25240, Turkey; (E.A.); (H.B.B.)
| | - İbrahim Sevki Bayrakdar
- Center of Research and Application for Computer Aided Diagnosis and Treatment in Health, Eskisehir Osmangazi University, Eskisehir 26040, Turkey;
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University, Eskisehir 26040, Turkey
| | - Kaan Orhan
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara 06620, Turkey;
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Nair SSK, David LR, Shariff A, Maskari SA, Mawali AA, Weis S, Fouad T, Ozsahin DU, Alshuweihi A, Obaideen A, Elshami W. CovMediScanX: A medical imaging solution for COVID-19 diagnosis from chest X-ray images. J Med Imaging Radiat Sci 2024; 55:272-280. [PMID: 38594085 DOI: 10.1016/j.jmir.2024.03.046] [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: 09/27/2023] [Revised: 02/17/2024] [Accepted: 03/19/2024] [Indexed: 04/11/2024]
Abstract
INTRODUCTION Radiologists have extensively employed the interpretation of chest X-rays (CXR) to identify visual markers indicative of COVID-19 infection, offering an alternative approach for the screening of infected individuals. This research article presents CovMediScanX, a deep learning-based framework designed for a rapid and automated diagnosis of COVID-19 from CXR scan images. METHODS The proposed approach encompasses gathering and preprocessing CXR image datasets, training deep learning-based custom-made Convolutional Neural Network (CNN), pre-trained and hybrid transfer learning models, identifying the highest-performing model based on key evaluation metrics, and embedding this model into a web interface called CovMediScanX, designed for radiologists to detect the COVID-19 status in new CXR images. RESULTS The custom-made CNN model obtained a remarkable testing accuracy of 94.32% outperforming other models. CovMediScanX, employing the custom-made CNN underwent evaluation with an independent dataset also. The images in the independent dataset are sourced from a scanning machine that is entirely different from those used for the training dataset, highlighting a clear distinction of datasets in their origins. The evaluation outcome highlighted the framework's capability to accurately detect COVID-19 cases, showcasing encouraging results with a precision of 73% and a recall of 84% for positive cases. However, the model requires further enhancement, particularly in improving its detection of normal cases, as evidenced by lower precision and recall rates. CONCLUSION The research proposes CovMediScanX framework that demonstrates promising potential in automatically identifying COVID-19 cases from CXR images. While the model's overall performance on independent data needs improvement, it is evident that addressing bias through the inclusion of diverse data sources during training could further enhance accuracy and reliability.
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Affiliation(s)
| | - Leena R David
- Department of Medical Diagnostic Imaging, College of Health Sciences, University of Sharjah, United Arab Emirates.
| | - Abdulwahid Shariff
- Department of Postgraduate Studies, University of Dar es Salaam, Tanzania
| | - Saqar Al Maskari
- Department of Computing and Electronics Engineering, Middle East College, Sultanate of Oman
| | - Adhra Al Mawali
- Quality Assurance and Planning, German University of Technology (GUtech), Sultanate of Oman
| | - Sammy Weis
- University Hospital, Sharjah, United Arab Emirates
| | - Taha Fouad
- University Hospital, Sharjah, United Arab Emirates
| | - Dilber Uzun Ozsahin
- Department of Medical Diagnostic Imaging, College of Health Sciences, University of Sharjah, United Arab Emirates
| | | | | | - Wiam Elshami
- Department of Medical Diagnostic Imaging, College of Health Sciences, University of Sharjah, United Arab Emirates
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Metta C, Beretta A, Pellungrini R, Rinzivillo S, Giannotti F. Towards Transparent Healthcare: Advancing Local Explanation Methods in Explainable Artificial Intelligence. Bioengineering (Basel) 2024; 11:369. [PMID: 38671790 PMCID: PMC11048122 DOI: 10.3390/bioengineering11040369] [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: 03/15/2024] [Revised: 04/09/2024] [Accepted: 04/10/2024] [Indexed: 04/28/2024] Open
Abstract
This paper focuses on the use of local Explainable Artificial Intelligence (XAI) methods, particularly the Local Rule-Based Explanations (LORE) technique, within healthcare and medical settings. It emphasizes the critical role of interpretability and transparency in AI systems for diagnosing diseases, predicting patient outcomes, and creating personalized treatment plans. While acknowledging the complexities and inherent trade-offs between interpretability and model performance, our work underscores the significance of local XAI methods in enhancing decision-making processes in healthcare. By providing granular, case-specific insights, local XAI methods like LORE enhance physicians' and patients' understanding of machine learning models and their outcome. Our paper reviews significant contributions to local XAI in healthcare, highlighting its potential to improve clinical decision making, ensure fairness, and comply with regulatory standards.
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Affiliation(s)
- Carlo Metta
- Institute of Information Science and Technologies (ISTI-CNR), Via Moruzzi 1, 56127 Pisa, Italy; (A.B.); (S.R.)
| | - Andrea Beretta
- Institute of Information Science and Technologies (ISTI-CNR), Via Moruzzi 1, 56127 Pisa, Italy; (A.B.); (S.R.)
| | - Roberto Pellungrini
- Faculty of Sciences, Scuola Normale Superiore, P.za dei Cavalieri 7, 56126 Pisa, Italy; (R.P.); (F.G.)
| | - Salvatore Rinzivillo
- Institute of Information Science and Technologies (ISTI-CNR), Via Moruzzi 1, 56127 Pisa, Italy; (A.B.); (S.R.)
| | - Fosca Giannotti
- Faculty of Sciences, Scuola Normale Superiore, P.za dei Cavalieri 7, 56126 Pisa, Italy; (R.P.); (F.G.)
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Metta C, Beretta A, Guidotti R, Yin Y, Gallinari P, Rinzivillo S, Giannotti F. Advancing Dermatological Diagnostics: Interpretable AI for Enhanced Skin Lesion Classification. Diagnostics (Basel) 2024; 14:753. [PMID: 38611666 PMCID: PMC11011805 DOI: 10.3390/diagnostics14070753] [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: 03/19/2024] [Revised: 03/30/2024] [Accepted: 03/30/2024] [Indexed: 04/14/2024] Open
Abstract
A crucial challenge in critical settings like medical diagnosis is making deep learning models used in decision-making systems interpretable. Efforts in Explainable Artificial Intelligence (XAI) are underway to address this challenge. Yet, many XAI methods are evaluated on broad classifiers and fail to address complex, real-world issues, such as medical diagnosis. In our study, we focus on enhancing user trust and confidence in automated AI decision-making systems, particularly for diagnosing skin lesions, by tailoring an XAI method to explain an AI model's ability to identify various skin lesion types. We generate explanations using synthetic images of skin lesions as examples and counterexamples, offering a method for practitioners to pinpoint the critical features influencing the classification outcome. A validation survey involving domain experts, novices, and laypersons has demonstrated that explanations increase trust and confidence in the automated decision system. Furthermore, our exploration of the model's latent space reveals clear separations among the most common skin lesion classes, a distinction that likely arises from the unique characteristics of each class and could assist in correcting frequent misdiagnoses by human professionals.
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Affiliation(s)
- Carlo Metta
- Institute of Information Science and Technologies (ISTI-CNR), 56124 Pisa, Italy; (A.B.); (S.R.)
| | - Andrea Beretta
- Institute of Information Science and Technologies (ISTI-CNR), 56124 Pisa, Italy; (A.B.); (S.R.)
| | - Riccardo Guidotti
- Department of Computer Science, Universitá di Pisa, 56124 Pisa, Italy;
| | - Yuan Yin
- Laboratoire d’Informatique de Paris 6, Sorbonne Université, 75005 Paris, Italy; (Y.Y.); (P.G.)
| | - Patrick Gallinari
- Laboratoire d’Informatique de Paris 6, Sorbonne Université, 75005 Paris, Italy; (Y.Y.); (P.G.)
| | - Salvatore Rinzivillo
- Institute of Information Science and Technologies (ISTI-CNR), 56124 Pisa, Italy; (A.B.); (S.R.)
| | - Fosca Giannotti
- Faculty of Sciences, Scuola Normale Superiore di Pisa, 56126 Paris, Italy;
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Lindroth H, Nalaie K, Raghu R, Ayala IN, Busch C, Bhattacharyya A, Moreno Franco P, Diedrich DA, Pickering BW, Herasevich V. Applied Artificial Intelligence in Healthcare: A Review of Computer Vision Technology Application in Hospital Settings. J Imaging 2024; 10:81. [PMID: 38667979 PMCID: PMC11050909 DOI: 10.3390/jimaging10040081] [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: 01/31/2024] [Revised: 03/08/2024] [Accepted: 03/11/2024] [Indexed: 04/28/2024] Open
Abstract
Computer vision (CV), a type of artificial intelligence (AI) that uses digital videos or a sequence of images to recognize content, has been used extensively across industries in recent years. However, in the healthcare industry, its applications are limited by factors like privacy, safety, and ethical concerns. Despite this, CV has the potential to improve patient monitoring, and system efficiencies, while reducing workload. In contrast to previous reviews, we focus on the end-user applications of CV. First, we briefly review and categorize CV applications in other industries (job enhancement, surveillance and monitoring, automation, and augmented reality). We then review the developments of CV in the hospital setting, outpatient, and community settings. The recent advances in monitoring delirium, pain and sedation, patient deterioration, mechanical ventilation, mobility, patient safety, surgical applications, quantification of workload in the hospital, and monitoring for patient events outside the hospital are highlighted. To identify opportunities for future applications, we also completed journey mapping at different system levels. Lastly, we discuss the privacy, safety, and ethical considerations associated with CV and outline processes in algorithm development and testing that limit CV expansion in healthcare. This comprehensive review highlights CV applications and ideas for its expanded use in healthcare.
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Affiliation(s)
- Heidi Lindroth
- Division of Nursing Research, Department of Nursing, Mayo Clinic, Rochester, MN 55905, USA; (K.N.); (R.R.); (I.N.A.); (C.B.)
- Center for Aging Research, Regenstrief Institute, School of Medicine, Indiana University, Indianapolis, IN 46202, USA
- Center for Health Innovation and Implementation Science, School of Medicine, Indiana University, Indianapolis, IN 46202, USA
| | - Keivan Nalaie
- Division of Nursing Research, Department of Nursing, Mayo Clinic, Rochester, MN 55905, USA; (K.N.); (R.R.); (I.N.A.); (C.B.)
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN 55905, USA; (D.A.D.); (B.W.P.); (V.H.)
| | - Roshini Raghu
- Division of Nursing Research, Department of Nursing, Mayo Clinic, Rochester, MN 55905, USA; (K.N.); (R.R.); (I.N.A.); (C.B.)
| | - Ivan N. Ayala
- Division of Nursing Research, Department of Nursing, Mayo Clinic, Rochester, MN 55905, USA; (K.N.); (R.R.); (I.N.A.); (C.B.)
| | - Charles Busch
- Division of Nursing Research, Department of Nursing, Mayo Clinic, Rochester, MN 55905, USA; (K.N.); (R.R.); (I.N.A.); (C.B.)
- College of Engineering, University of Wisconsin-Madison, Madison, WI 53705, USA
| | | | - Pablo Moreno Franco
- Department of Transplantation Medicine, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Daniel A. Diedrich
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN 55905, USA; (D.A.D.); (B.W.P.); (V.H.)
| | - Brian W. Pickering
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN 55905, USA; (D.A.D.); (B.W.P.); (V.H.)
| | - Vitaly Herasevich
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN 55905, USA; (D.A.D.); (B.W.P.); (V.H.)
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Liu M, Wu T, Li X, Zhu Y, Chen S, Huang J, Zhou F, Liu H. ACPPfel: Explainable deep ensemble learning for anticancer peptides prediction based on feature optimization. Front Genet 2024; 15:1352504. [PMID: 38487252 PMCID: PMC10937565 DOI: 10.3389/fgene.2024.1352504] [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: 12/13/2023] [Accepted: 02/19/2024] [Indexed: 03/17/2024] Open
Abstract
Background: Cancer is a significant global health problem that continues to cause a high number of deaths worldwide. Traditional cancer treatments often come with risks that can compromise the functionality of vital organs. As a potential alternative to these conventional therapies, Anticancer peptides (ACPs) have garnered attention for their small size, high specificity, and reduced toxicity, making them as a promising option for cancer treatments. Methods: However, the process of identifying effective ACPs through wet-lab screening experiments is time-consuming and requires a lot of labor. To overcome this challenge, a deep ensemble learning method is constructed to predict anticancer peptides (ACPs) in this study. To evaluate the reliability of the framework, four different datasets are used in this study for training and testing. During the training process of the model, integration of feature selection methods, feature dimensionality reduction measures, and optimization of the deep ensemble model are carried out. Finally, we explored the interpretability of features that affected the final prediction results and built a web server platform to facilitate anticancer peptides prediction, which can be used by all researchers for further studies. This web server can be accessed at http://lmylab.online:5001/. Results: The result of this study achieves an accuracy rate of 98.53% and an AUC (Area under Curve) value of 0.9972 on the ACPfel dataset, it has improvements on other datasets as well.
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Affiliation(s)
- Mingyou Liu
- School of Biology and Engineering (School of Health Medicine Modern Industry), Guizhou Medical University, Guiyang, China
- Engineering Research Center of Health Medicine Biotechnology of Guizhou Province, Guizhou Medical University, Guiyang, China
| | - Tao Wu
- School of Biology and Engineering (School of Health Medicine Modern Industry), Guizhou Medical University, Guiyang, China
| | - Xue Li
- School of Biology and Engineering (School of Health Medicine Modern Industry), Guizhou Medical University, Guiyang, China
- Engineering Research Center of Health Medicine Biotechnology of Guizhou Province, Guizhou Medical University, Guiyang, China
| | - Yingxue Zhu
- School of Biology and Engineering (School of Health Medicine Modern Industry), Guizhou Medical University, Guiyang, China
- Engineering Research Center of Health Medicine Biotechnology of Guizhou Province, Guizhou Medical University, Guiyang, China
| | - Sen Chen
- School of Biology and Engineering (School of Health Medicine Modern Industry), Guizhou Medical University, Guiyang, China
| | - Jian Huang
- School of Life Science and Technology, University of Electronic Science and Technology, Chengdu, China
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, China
| | - Fengfeng Zhou
- School of Biology and Engineering (School of Health Medicine Modern Industry), Guizhou Medical University, Guiyang, China
- College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China
| | - Hongmei Liu
- School of Biology and Engineering (School of Health Medicine Modern Industry), Guizhou Medical University, Guiyang, China
- Engineering Research Center of Health Medicine Biotechnology of Guizhou Province, Guizhou Medical University, Guiyang, China
- College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China
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Liu M, Liu H, Wu T, Zhu Y, Zhou Y, Huang Z, Xiang C, Huang J. ACP-Dnnel: anti-coronavirus peptides' prediction based on deep neural network ensemble learning. Amino Acids 2023; 55:1121-1136. [PMID: 37402073 DOI: 10.1007/s00726-023-03300-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 06/25/2023] [Indexed: 07/05/2023]
Abstract
The ongoing COVID-19 pandemic has caused dramatic loss of human life. There is an urgent need for safe and efficient anti-coronavirus infection drugs. Anti-coronavirus peptides (ACovPs) can inhibit coronavirus infection. With high-efficiency, low-toxicity, and broad-spectrum inhibitory effects on coronaviruses, they are promising candidates to be developed into a new type of anti-coronavirus drug. Experiment is the traditional way of ACovPs' identification, which is less efficient and more expensive. With the accumulation of experimental data on ACovPs, computational prediction provides a cheaper and faster way to find anti-coronavirus peptides' candidates. In this study, we ensemble several state-of-the-art machine learning methodologies to build nine classification models for the prediction of ACovPs. These models were pre-trained using deep neural networks, and the performance of our ensemble model, ACP-Dnnel, was evaluated across three datasets and independent dataset. We followed Chou's 5-step rules. (1) we constructed the benchmark datasets data1, data2, and data3 for training and testing, and introduced the independent validation dataset ACVP-M; (2) we analyzed the peptides sequence composition feature of the benchmark dataset; (3) we constructed the ACP-Dnnel model with deep convolutional neural network (DCNN) merged the bi-directional long short-term memory (BiLSTM) as the base model for pre-training to extract the features embedded in the benchmark dataset, and then, nine classification algorithms were introduced to ensemble together for classification prediction and voting together; (4) tenfold cross-validation was introduced during the training process, and the final model performance was evaluated; (5) finally, we constructed a user-friendly web server accessible to the public at http://150.158.148.228:5000/ . The highest accuracy (ACC) of ACP-Dnnel reaches 97%, and the Matthew's correlation coefficient (MCC) value exceeds 0.9. On three different datasets, its average accuracy is 96.0%. After the latest independent dataset validation, ACP-Dnnel improved at MCC, SP, and ACC values 6.2%, 7.5% and 6.3% greater, respectively. It is suggested that ACP-Dnnel can be helpful for the laboratory identification of ACovPs, speeding up the anti-coronavirus peptide drug discovery and development. We constructed the web server of anti-coronavirus peptides' prediction and it is available at http://150.158.148.228:5000/ .
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Affiliation(s)
- Mingyou Liu
- School of Biology and Engineering, Guizhou Medical University, Guiyang, Guizhou, China
- School of Life Science and Technology, University of Electronic Science and Technology, Chengdu, Sichuan, China
| | - Hongmei Liu
- School of Biology and Engineering, Guizhou Medical University, Guiyang, Guizhou, China
| | - Tao Wu
- School of Biology and Engineering, Guizhou Medical University, Guiyang, Guizhou, China
| | - Yingxue Zhu
- School of Biology and Engineering, Guizhou Medical University, Guiyang, Guizhou, China
| | - Yuwei Zhou
- School of Life Science and Technology, University of Electronic Science and Technology, Chengdu, Sichuan, China
| | - Ziru Huang
- School of Life Science and Technology, University of Electronic Science and Technology, Chengdu, Sichuan, China
| | - Changcheng Xiang
- School of Computer Science and Technology, Aba Teachers University, Aba, Sichuan, China.
| | - Jian Huang
- School of Life Science and Technology, University of Electronic Science and Technology, Chengdu, Sichuan, China.
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, Sichuan, China.
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Khan ZA, Hussain T, Ullah A, Ullah W, Del Ser J, Muhammad K, Sajjad M, Baik SW. Modelling Electricity Consumption During the COVID19 Pandemic: Datasets, Models, Results and a Research Agenda. ENERGY AND BUILDINGS 2023; 294:113204. [PMID: 37342253 PMCID: PMC10226901 DOI: 10.1016/j.enbuild.2023.113204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 05/15/2023] [Accepted: 05/25/2023] [Indexed: 06/22/2023]
Abstract
The COVID19 pandemic has impacted the global economy, social activities, and Electricity Consumption (EC), affecting the performance of historical data-based Electricity Load Forecasting (ELF) algorithms. This study thoroughly analyses the pandemic's impact on these models and develop a hybrid model with better prediction accuracy using COVID19 data. Existing datasets are reviewed, and their limited generalization potential for the COVID19 period is highlighted. A dataset of 96 residential customers, comprising 36 and six months before and after the pandemic, is collected, posing significant challenges for current models. The proposed model employs convolutional layers for feature extraction, gated recurrent nets for temporal feature learning, and a self-attention module for feature selection, leading to better generalization for predicting EC patterns. Our proposed model outperforms existing models, as demonstrated by a detailed ablation study using our dataset. For instance, it achieves an average reduction of 0.56% & 3.46% in MSE, 1.5% & 5.07% in RMSE, and 11.81% & 13.19% in MAPE over the pre- and post-pandemic data, respectively. However, further research is required to address the varied nature of the data. These findings have significant implications for improving ELF algorithms during pandemics and other significant events that disrupt historical data patterns.
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Affiliation(s)
| | - Tanveer Hussain
- Institute for Transport Studies, University of Leeds, LS2 9JT Leeds, UK
| | - Amin Ullah
- CoRIS Institute, Oregon State University, Corvallis 97331, OR, USA
| | - Waseem Ullah
- Sejong University, Seoul 143-747, Republic of Korea
| | - Javier Del Ser
- TECNALIA (Basque Research & Technology Alliance - BRTA), P. Tecnologico, Ed. 700, 48160 Derio, Bizkaia, Spain
- University of the Basque Country (UPV/EHU), 48013 Bilbao, Bizkaia, Spain
| | - Khan Muhammad
- Visual Analytics for Knowledge Laboratory (VIS2KNOW Lab), Department of Applied Artificial Intelligence, College of Computing and Informatics, Sungkyunkwan University, Seoul 03063, South Korea
| | - Muhammad Sajjad
- Digital Image Processing Laboratory, Department of Computer Science, Islamia College Peshawar, Peshawar 25000, Pakistan
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Shaheed K, Abbas Q, Hussain A, Qureshi I. Optimized Xception Learning Model and XgBoost Classifier for Detection of Multiclass Chest Disease from X-ray Images. Diagnostics (Basel) 2023; 13:2583. [PMID: 37568946 PMCID: PMC10416977 DOI: 10.3390/diagnostics13152583] [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: 07/06/2023] [Revised: 07/23/2023] [Accepted: 07/31/2023] [Indexed: 08/13/2023] Open
Abstract
Computed tomography (CT) scans, or radiographic images, were used to aid in the early diagnosis of patients and detect normal and abnormal lung function in the human chest. However, the diagnosis of lungs infected with coronavirus disease 2019 (COVID-19) was made more accurately from CT scan data than from a swab test. This study uses human chest radiography pictures to identify and categorize normal lungs, lung opacities, COVID-19-infected lungs, and viral pneumonia (often called pneumonia). In the past, several CAD systems using image processing, ML/DL, and other forms of machine learning have been developed. However, those CAD systems did not provide a general solution, required huge hyper-parameters, and were computationally inefficient to process huge datasets. Moreover, the DL models required high computational complexity, which requires a huge memory cost, and the complexity of the experimental materials' backgrounds, which makes it difficult to train an efficient model. To address these issues, we developed the Inception module, which was improved to recognize and detect four classes of Chest X-ray in this research by substituting the original convolutions with an architecture based on modified-Xception (m-Xception). In addition, the model incorporates depth-separable convolution layers within the convolution layer, interlinked by linear residuals. The model's training utilized a two-stage transfer learning process to produce an effective model. Finally, we used the XgBoost classifier to recognize multiple classes of chest X-rays. To evaluate the m-Xception model, the 1095 dataset was converted using a data augmentation technique into 48,000 X-ray images, including 12,000 normal, 12,000 pneumonia, 12,000 COVID-19 images, and 12,000 lung opacity images. To balance these classes, we used a data augmentation technique. Using public datasets with three distinct train-test divisions (80-20%, 70-30%, and 60-40%) to evaluate our work, we attained an average of 96.5% accuracy, 96% F1 score, 96% recall, and 96% precision. A comparative analysis demonstrates that the m-Xception method outperforms comparable existing methods. The results of the experiments indicate that the proposed approach is intended to assist radiologists in better diagnosing different lung diseases.
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Affiliation(s)
- Kashif Shaheed
- Department of Multimedia Systems, Faculty of Electronics, Telecommunication and Informatics, Gdansk University of Technology, 80-233 Gdansk, Poland;
| | - Qaisar Abbas
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia;
| | - Ayyaz Hussain
- Department of Computer Science, Quaid-i-Azam University, Islamabad 44000, Pakistan;
| | - Imran Qureshi
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia;
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11
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Saqib MA, Aqib M, Tahir MN, Hafeez Y. Towards deep learning based smart farming for intelligent weeds management in crops. FRONTIERS IN PLANT SCIENCE 2023; 14:1211235. [PMID: 37575940 PMCID: PMC10416644 DOI: 10.3389/fpls.2023.1211235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 06/29/2023] [Indexed: 08/15/2023]
Abstract
Introduction Deep learning (DL) is a core constituent for building an object detection system and provides a variety of algorithms to be used in a variety of applications. In agriculture, weed management is one of the major concerns, weed detection systems could be of great help to improve production. In this work, we have proposed a DL-based weed detection model that can efficiently be used for effective weed management in crops. Methods Our proposed model uses Convolutional Neural Network based object detection system You Only Look Once (YOLO) for training and prediction. The collected dataset contains RGB images of four different weed species named Grass, Creeping Thistle, Bindweed, and California poppy. This dataset is manipulated by applying LAB (Lightness A and B) and HSV (Hue, Saturation, Value) image transformation techniques and then trained on four YOLO models (v3, v3-tiny, v4, v4-tiny). Results and discussion The effects of image transformation are analyzed, and it is deduced that the model performance is not much affected by this transformation. Inferencing results obtained by making a comparison of correctly predicted weeds are quite promising, among all models implemented in this work, the YOLOv4 model has achieved the highest accuracy. It has correctly predicted 98.88% weeds with an average loss of 1.8 and 73.1% mean average precision value. Future work In the future, we plan to integrate this model in a variable rate sprayer for precise weed management in real time.
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Affiliation(s)
- Muhammad Ali Saqib
- University Institute of Information Technology (UIIT), Pir Mehr Ali Shah (PMAS)-Arid Agriculture University Rawalpindi, Rawalpindi, Punjab, Pakistan
| | - Muhammad Aqib
- University Institute of Information Technology (UIIT), Pir Mehr Ali Shah (PMAS)-Arid Agriculture University Rawalpindi, Rawalpindi, Punjab, Pakistan
- National Center of Industrial Biotechnology, Pir Mehr Ali Shah (PMAS)-Arid Agriculture University Rawalpindi, Rawalpindi, Punjab, Pakistan
| | - Muhammad Naveed Tahir
- Department of Agronomy, Pir Mehr Ali Shah (PMAS)-Arid Agriculture University Rawalpindi, Rawalpindi, Punjab, Pakistan
- Pilot Project for Data Driven Smart Decision Platform for Increased Agriculture Productivity, Pir Mehr Ali Shah (PMAS)-Arid Agriculture University Rawalpindi, Rawalpindi, Punjab, Pakistan
| | - Yaser Hafeez
- University Institute of Information Technology (UIIT), Pir Mehr Ali Shah (PMAS)-Arid Agriculture University Rawalpindi, Rawalpindi, Punjab, Pakistan
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12
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Mehrdad S, Shamout FE, Wang Y, Atashzar SF. Deep learning for deterioration prediction of COVID-19 patients based on time-series of three vital signs. Sci Rep 2023; 13:9968. [PMID: 37339986 DOI: 10.1038/s41598-023-37013-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Accepted: 06/14/2023] [Indexed: 06/22/2023] Open
Abstract
Unrecognized deterioration of COVID-19 patients can lead to high morbidity and mortality. Most existing deterioration prediction models require a large number of clinical information, typically collected in hospital settings, such as medical images or comprehensive laboratory tests. This is infeasible for telehealth solutions and highlights a gap in deterioration prediction models based on minimal data, which can be recorded at a large scale in any clinic, nursing home, or even at the patient's home. In this study, we develop and compare two prognostic models that predict if a patient will experience deterioration in the forthcoming 3 to 24 h. The models sequentially process routine triadic vital signs: (a) oxygen saturation, (b) heart rate, and (c) temperature. These models are also provided with basic patient information, including sex, age, vaccination status, vaccination date, and status of obesity, hypertension, or diabetes. The difference between the two models is the way that the temporal dynamics of the vital signs are processed. Model #1 utilizes a temporally-dilated version of the Long-Short Term Memory model (LSTM) for temporal processes, and Model #2 utilizes a residual temporal convolutional network (TCN) for this purpose. We train and evaluate the models using data collected from 37,006 COVID-19 patients at NYU Langone Health in New York, USA. The convolution-based model outperforms the LSTM based model, achieving a high AUROC of 0.8844-0.9336 for 3 to 24 h deterioration prediction on a held-out test set. We also conduct occlusion experiments to evaluate the importance of each input feature, which reveals the significance of continuously monitoring the variation of the vital signs. Our results show the prospect for accurate deterioration forecast using a minimum feature set that can be relatively easily obtained using wearable devices and self-reported patient information.
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Affiliation(s)
- Sarmad Mehrdad
- Department of Electrical and Computer Engineering, New York University (NYU), New York, USA
| | - Farah E Shamout
- Department of Biomedical Engineering, New York University (NYU), New York, USA
- Division of Engineering, New York University Abu Dhabi (NYUAD), Abu Dhabi, UAE
- Computer Science and Engineering, New York University (NYU), New York, USA
| | - Yao Wang
- Department of Electrical and Computer Engineering, New York University (NYU), New York, USA
- Department of Biomedical Engineering, New York University (NYU), New York, USA
| | - S Farokh Atashzar
- Department of Electrical and Computer Engineering, New York University (NYU), New York, USA.
- Department of Biomedical Engineering, New York University (NYU), New York, USA.
- Department of Mechanical and Aerospace Engineering, New York University (NYU), New York, USA.
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13
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Sultana A, Nahiduzzaman M, Bakchy SC, Shahriar SM, Peyal HI, Chowdhury MEH, Khandakar A, Arselene Ayari M, Ahsan M, Haider J. A Real Time Method for Distinguishing COVID-19 Utilizing 2D-CNN and Transfer Learning. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23094458. [PMID: 37177662 PMCID: PMC10181786 DOI: 10.3390/s23094458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 04/19/2023] [Accepted: 04/21/2023] [Indexed: 05/15/2023]
Abstract
Rapid identification of COVID-19 can assist in making decisions for effective treatment and epidemic prevention. The PCR-based test is expert-dependent, is time-consuming, and has limited sensitivity. By inspecting Chest R-ray (CXR) images, COVID-19, pneumonia, and other lung infections can be detected in real time. The current, state-of-the-art literature suggests that deep learning (DL) is highly advantageous in automatic disease classification utilizing the CXR images. The goal of this study is to develop models by employing DL models for identifying COVID-19 and other lung disorders more efficiently. For this study, a dataset of 18,564 CXR images with seven disease categories was created from multiple publicly available sources. Four DL architectures including the proposed CNN model and pretrained VGG-16, VGG-19, and Inception-v3 models were applied to identify healthy and six lung diseases (fibrosis, lung opacity, viral pneumonia, bacterial pneumonia, COVID-19, and tuberculosis). Accuracy, precision, recall, f1 score, area under the curve (AUC), and testing time were used to evaluate the performance of these four models. The results demonstrated that the proposed CNN model outperformed all other DL models employed for a seven-class classification with an accuracy of 93.15% and average values for precision, recall, f1-score, and AUC of 0.9343, 0.9443, 0.9386, and 0.9939. The CNN model equally performed well when other multiclass classifications including normal and COVID-19 as the common classes were considered, yielding accuracy values of 98%, 97.49%, 97.81%, 96%, and 96.75% for two, three, four, five, and six classes, respectively. The proposed model can also identify COVID-19 with shorter training and testing times compared to other transfer learning models.
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Affiliation(s)
- Abida Sultana
- Department of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
| | - Md Nahiduzzaman
- Department of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
| | - Sagor Chandro Bakchy
- Department of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
| | - Saleh Mohammed Shahriar
- Department of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
| | - Hasibul Islam Peyal
- Department of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
| | | | - Amith Khandakar
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
| | | | - Mominul Ahsan
- Department of Computer Science, University of York, Deramore Lane, Heslington, York YO10 5GH, UK
| | - Julfikar Haider
- Department of Engineering, Manchester Metropolitan University, Chester Street, Manchester M1 5GD, UK
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14
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Chen F, Shi H, Yang J, Lai Y, Han J, Chen Y. A new method to identifying optimal adjustment strategy when the car cockpit is uncomfortable: optimal state distance method. PeerJ Comput Sci 2023; 9:e1324. [PMID: 37346597 PMCID: PMC10280415 DOI: 10.7717/peerj-cs.1324] [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: 12/27/2022] [Accepted: 03/13/2023] [Indexed: 06/23/2023]
Abstract
With the rapid development of the automobile industry, the comfort of the cockpit has become the standard for judging the quality of the car. People have also put forward higher requirements for cockpit comfort. In the process of driving, the cockpit environment will constantly change, and the comfort will also change. When the comprehensive comfort level of the cockpit decreases and the occupants feel uncomfortable, the cockpit comfort should be adjusted. In this article, a cockpit comfort evaluation model is established to realize the evaluation of cockpit comfort. In addition, we elaborate the theory of optimal state distance, where the numerical magnitude of the optimal state distance is used to reflect the extent to which an indicator deviates from its optimal state. Also, a cockpit optimal adjustment strategy identification model is established based on the theory, which can obtain the optimal adjustment strategy in a certain cockpit operating environment, facilitate the timely adjustment of the corresponding actuator, and realize the dynamic monitoring and adjustment of cockpit comfort. This project provides a reference direction for cockpit comfort adjustment, which is of great significance for future research and development of automotive cockpit comfort.
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Affiliation(s)
- Fei Chen
- School of Automobile and Transportation, Xihua University, Chengdu, Sichuan Province, China
- Vehicle Measurement, Control and Safety Key Laboratory of Sichuan Province, Xihua University, Chengdu, China
- Provincial Engineering Research Center for New Energy Vehicle Intelligent Control and Simulation Test Technology of Sichuan, Xihua University, Chengdu, China
| | - Hongbo Shi
- School of Automobile and Transportation, Xihua University, Chengdu, Sichuan Province, China
| | - Jianjun Yang
- School of Automobile and Transportation, Xihua University, Chengdu, Sichuan Province, China
- Vehicle Measurement, Control and Safety Key Laboratory of Sichuan Province, Xihua University, Chengdu, China
- Provincial Engineering Research Center for New Energy Vehicle Intelligent Control and Simulation Test Technology of Sichuan, Xihua University, Chengdu, China
| | - Yu Lai
- School of Mechanical Engineering, Xihua University, Chengdu, Sichuan Province, China
| | - Jiahao Han
- School of Automobile and Transportation, Xihua University, Chengdu, Sichuan Province, China
| | - Yimeng Chen
- School of Automobile and Transportation, Xihua University, Chengdu, Sichuan Province, China
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15
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Dobrijević D, Andrijević L, Antić J, Rakić G, Pastor K. Hemogram-based decision tree models for discriminating COVID-19 from RSV in infants. J Clin Lab Anal 2023; 37:e24862. [PMID: 36972470 PMCID: PMC10156096 DOI: 10.1002/jcla.24862] [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: 12/20/2022] [Revised: 12/29/2022] [Accepted: 03/04/2023] [Indexed: 03/29/2023] Open
Abstract
OBJECTIVE Decision trees are efficient and reliable decision-making algorithms, and medicine has reached its peak of interest in these methods during the current pandemic. Herein, we reported several decision tree algorithms for a rapid discrimination between coronavirus disease (COVID-19) and respiratory syncytial virus (RSV) infection in infants. METHODS A cross-sectional study was conducted on 77 infants: 33 infants with novel betacoronavirus (SARS-CoV-2) infection and 44 infants with RSV infection. In total, 23 hemogram-based instances were used to construct the decision tree models via 10-fold cross-validation method. RESULTS The Random forest model showed the highest accuracy (81.8%), while in terms of sensitivity (72.7%), specificity (88.6%), positive predictive value (82.8%), and negative predictive value (81.3%), the optimized forest model was the most superior one. CONCLUSION Random forest and optimized forest models might have significant clinical applications, helping to speed up decision-making when SARS-CoV-2 and RSV are suspected, prior to molecular genome sequencing and/or antigen testing.
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Affiliation(s)
- Dejan Dobrijević
- Faculty of Medicine, University of Novi Sad, Novi Sad, Serbia
- Institute for Child and Youth Health Care of Vojvodina, Novi Sad, Serbia
| | | | - Jelena Antić
- Faculty of Medicine, University of Novi Sad, Novi Sad, Serbia
- Institute for Child and Youth Health Care of Vojvodina, Novi Sad, Serbia
| | - Goran Rakić
- Faculty of Medicine, University of Novi Sad, Novi Sad, Serbia
- Institute for Child and Youth Health Care of Vojvodina, Novi Sad, Serbia
| | - Kristian Pastor
- Faculty of Technology, Univeristy of Novi Sad, Novi Sad, Serbia
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16
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Farhan AMQ, Yang S. Automatic lung disease classification from the chest X-ray images using hybrid deep learning algorithm. MULTIMEDIA TOOLS AND APPLICATIONS 2023:1-27. [PMID: 37362647 PMCID: PMC10030349 DOI: 10.1007/s11042-023-15047-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 08/30/2022] [Accepted: 02/27/2023] [Indexed: 06/28/2023]
Abstract
The chest X-ray images provide vital information about the congestion cost-effectively. We propose a novel Hybrid Deep Learning Algorithm (HDLA) framework for automatic lung disease classification from chest X-ray images. The model consists of steps including pre-processing of chest X-ray images, automatic feature extraction, and detection. In a pre-processing step, our goal is to improve the quality of raw chest X-ray images using the combination of optimal filtering without data loss. The robust Convolutional Neural Network (CNN) is proposed using the pre-trained model for automatic lung feature extraction. We employed the 2D CNN model for the optimum feature extraction in minimum time and space requirements. The proposed 2D CNN model ensures robust feature learning with highly efficient 1D feature estimation from the input pre-processed image. As the extracted 1D features have suffered from significant scale variations, we optimized them using min-max scaling. We classify the CNN features using the different machine learning classifiers such as AdaBoost, Support Vector Machine (SVM), Random Forest (RM), Backpropagation Neural Network (BNN), and Deep Neural Network (DNN). The experimental results claim that the proposed model improves the overall accuracy by 3.1% and reduces the computational complexity by 16.91% compared to state-of-the-art methods.
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Affiliation(s)
- Abobaker Mohammed Qasem Farhan
- School of information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Shangming Yang
- School of information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, China
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17
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Alam Suha S, Islam MN. Exploring the Dominant Features and Data-driven Detection of Polycystic Ovary Syndrome through Modified Stacking Ensemble Machine Learning Technique. Heliyon 2023; 9:e14518. [PMID: 36994397 PMCID: PMC10040521 DOI: 10.1016/j.heliyon.2023.e14518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 01/09/2023] [Accepted: 03/09/2023] [Indexed: 03/18/2023] Open
Abstract
Polycystic ovary syndrome (PCOS) is the most frequent endocrinological anomaly in reproductive women that causes persistent hormonal secretion disruption, leading to the formation of numerous cysts within the ovaries and serious health complications. But the real-world clinical detection technique for PCOS is very critical since the accuracy of interpretations being substantially dependent on the physician's expertise. Thus, an artificially intelligent PCOS prediction model might be a feasible additional technique to the error prone and time-consuming diagnostic technique. In this study, a modified ensemble machine learning (ML) classification approach is proposed utilizing state-of-the-art stacking technique for PCOS identification with patients' symptom data; employing five traditional ML models as base learners and then one bagging or boosting ensemble ML model as the meta-learner of the stacked model. Furthermore, three distinct types of feature selection strategies are applied to pick different sets of features with varied numbers and combinations of attributes. To evaluate and explore the dominant features necessary for predicting PCOS, the proposed technique with five variety of models and other ten types of classifiers is trained, tested and assessed utilizing different feature sets. As outcomes, the proposed stacking ensemble technique significantly enhances the accuracy in comparison to the other existing ML based techniques in case of all varieties of feature sets. However, among various models investigated to categorize PCOS and non-PCOS patients, the stacking ensemble model with 'Gradient Boosting' classifier as meta learner outperforms others with 95.7% accuracy while utilizing the top 25 features selected using Principal Component Analysis (PCA) feature selection technique.
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18
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Cai C, Gou B, Khishe M, Mohammadi M, Rashidi S, Moradpour R, Mirjalili S. Improved deep convolutional neural networks using chimp optimization algorithm for Covid19 diagnosis from the X-ray images. EXPERT SYSTEMS WITH APPLICATIONS 2023; 213:119206. [PMID: 36348736 PMCID: PMC9633109 DOI: 10.1016/j.eswa.2022.119206] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 09/17/2022] [Accepted: 10/31/2022] [Indexed: 05/11/2023]
Abstract
Applying Deep Learning (DL) in radiological images (i.e., chest X-rays) is emerging because of the necessity of having accurate and fast COVID-19 detectors. Deep Convolutional Neural Networks (DCNN) have been typically used as robust COVID-19 positive case detectors in these approaches. Such DCCNs tend to utilize Gradient Descent-Based (GDB) algorithms as the last fully-connected layers' trainers. Although GDB training algorithms have simple structures and fast convergence rates for cases with large training samples, they suffer from the manual tuning of numerous parameters, getting stuck in local minima, large training samples set requirements, and inherently sequential procedures. It is exceedingly challenging to parallelize them with Graphics Processing Units (GPU). Consequently, the Chimp Optimization Algorithm (ChOA) is presented for training the DCNN's fully connected layers in light of the scarcity of a big COVID-19 training dataset and for the purpose of developing a fast COVID-19 detector with the capability of parallel implementation. In addition, two publicly accessible datasets termed COVID-Xray-5 k and COVIDetectioNet are used to benchmark the proposed detector known as DCCN-Chimp. In order to make a fair comparison, two structures are proposed: i-6c-2 s-12c-2 s and i-8c-2 s-16c-2 s, all of which have had their hyperparameters fine-tuned. The outcomes are evaluated in comparison to standard DCNN, Hybrid DCNN plus Genetic Algorithm (DCNN-GA), and Matched Subspace classifier with Adaptive Dictionaries (MSAD). Due to the large variation in results, we employ a weighted average of the ensemble of ten trained DCNN-ChOA, with the validation accuracy of the weights being used to determine the final weights. The validation accuracy for the mixed ensemble DCNN-ChOA is 99.11%. LeNet-5 DCNN's ensemble detection accuracy on COVID-19 is 84.58%. Comparatively, the suggested DCNN-ChOA yields over 99.11% accurate detection with a false alarm rate of less than 0.89%. The outcomes show that the DCCN-Chimp can deliver noticeably superior results than the comparable detectors. The Class Activation Map (CAM) is another tool used in this study to identify probable COVID-19-infected areas. Results show that highlighted regions are completely connected with clinical outcomes, which has been verified by experts.
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Affiliation(s)
- Chengfeng Cai
- School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, China
| | - Bingchen Gou
- School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, China
| | - Mohammad Khishe
- Departement of Electrical Engineering, Imam Khomeini Marine Science University, Nowshahr, Iran
| | - Mokhtar Mohammadi
- Department of Information Technology, College of Engineering and Computer Science, Lebanese French University, Kurdistan Region, Iraq
| | - Shima Rashidi
- Department of Computer Science, College of Science and Technology, University of Human Development, Sulaymaniyah, Kurdistan Region, Iraq
| | - Reza Moradpour
- Departement of Electrical Engineering, Imam Khomeini Marine Science University, Nowshahr, Iran
| | - Seyedali Mirjalili
- Centre for Artificial Intelligence Research and Optimization, Torrens University, Australia
- University Research and Innovation Center, Obuda University, 1034 Budapest, Hungary
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19
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Cai C, Gou B, Khishe M, Mohammadi M, Rashidi S, Moradpour R, Mirjalili S. Improved deep convolutional neural networks using chimp optimization algorithm for Covid19 diagnosis from the X-ray images. EXPERT SYSTEMS WITH APPLICATIONS 2023; 213:119206. [PMID: 36348736 DOI: 10.1016/j.eswa.2020.113338] [Citation(s) in RCA: 161] [Impact Index Per Article: 161.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 09/17/2022] [Accepted: 10/31/2022] [Indexed: 05/25/2023]
Abstract
Applying Deep Learning (DL) in radiological images (i.e., chest X-rays) is emerging because of the necessity of having accurate and fast COVID-19 detectors. Deep Convolutional Neural Networks (DCNN) have been typically used as robust COVID-19 positive case detectors in these approaches. Such DCCNs tend to utilize Gradient Descent-Based (GDB) algorithms as the last fully-connected layers' trainers. Although GDB training algorithms have simple structures and fast convergence rates for cases with large training samples, they suffer from the manual tuning of numerous parameters, getting stuck in local minima, large training samples set requirements, and inherently sequential procedures. It is exceedingly challenging to parallelize them with Graphics Processing Units (GPU). Consequently, the Chimp Optimization Algorithm (ChOA) is presented for training the DCNN's fully connected layers in light of the scarcity of a big COVID-19 training dataset and for the purpose of developing a fast COVID-19 detector with the capability of parallel implementation. In addition, two publicly accessible datasets termed COVID-Xray-5 k and COVIDetectioNet are used to benchmark the proposed detector known as DCCN-Chimp. In order to make a fair comparison, two structures are proposed: i-6c-2 s-12c-2 s and i-8c-2 s-16c-2 s, all of which have had their hyperparameters fine-tuned. The outcomes are evaluated in comparison to standard DCNN, Hybrid DCNN plus Genetic Algorithm (DCNN-GA), and Matched Subspace classifier with Adaptive Dictionaries (MSAD). Due to the large variation in results, we employ a weighted average of the ensemble of ten trained DCNN-ChOA, with the validation accuracy of the weights being used to determine the final weights. The validation accuracy for the mixed ensemble DCNN-ChOA is 99.11%. LeNet-5 DCNN's ensemble detection accuracy on COVID-19 is 84.58%. Comparatively, the suggested DCNN-ChOA yields over 99.11% accurate detection with a false alarm rate of less than 0.89%. The outcomes show that the DCCN-Chimp can deliver noticeably superior results than the comparable detectors. The Class Activation Map (CAM) is another tool used in this study to identify probable COVID-19-infected areas. Results show that highlighted regions are completely connected with clinical outcomes, which has been verified by experts.
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Affiliation(s)
- Chengfeng Cai
- School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, China
| | - Bingchen Gou
- School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, China
| | - Mohammad Khishe
- Departement of Electrical Engineering, Imam Khomeini Marine Science University, Nowshahr, Iran
| | - Mokhtar Mohammadi
- Department of Information Technology, College of Engineering and Computer Science, Lebanese French University, Kurdistan Region, Iraq
| | - Shima Rashidi
- Department of Computer Science, College of Science and Technology, University of Human Development, Sulaymaniyah, Kurdistan Region, Iraq
| | - Reza Moradpour
- Departement of Electrical Engineering, Imam Khomeini Marine Science University, Nowshahr, Iran
| | - Seyedali Mirjalili
- Centre for Artificial Intelligence Research and Optimization, Torrens University, Australia
- University Research and Innovation Center, Obuda University, 1034 Budapest, Hungary
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20
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Fayyaz AM, Raza M, Sharif M, Shah JH, Kadry S, Martínez OS. An Integrated Framework for COVID-19 Classification Based on Ensembles of Deep Features and Entropy Coded GLEO Feature Selection. INT J UNCERTAIN FUZZ 2023. [DOI: 10.1142/s0218488523500101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/05/2023]
Abstract
COVID-19 is a challenging worldwide pandemic disease nowadays that spreads from person to person in a very fast manner. It is necessary to develop an automated technique for COVID-19 identification. This work investigates a new framework that predicts COVID-19 based on X-ray images. The suggested methodology contains core phases as preprocessing, feature extraction, selection and categorization. The Guided and 2D Gaussian filters are utilized for image improvement as a preprocessing phase. The outcome is then passed to 2D-superpixel method for region of interest (ROI). The pre-trained models such as Darknet-53 and Densenet-201 are then applied for features extraction from the segmented images. The entropy coded GLEO features selection is based on the extracted and selected features, and ensemble serially to produce a single feature vector. The single vector is finally supplied as an input to the variations of the SVM classifier for the categorization of the normal/abnormal (COVID-19) X-rays images. The presented approach is evaluated with different measures known as accuracy, recall, F1 Score, and precision. The integrated framework for the proposed system achieves the acceptable accuracies on the SVM Classifiers, which authenticate the proposed approach’s effectiveness.
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Affiliation(s)
- Abdul Muiz Fayyaz
- Department of Computer Science, University of Wah, Wah Cantt 47040, Pakistan
| | - Mudassar Raza
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantt, 47040, Pakistan
| | - Muhammad Sharif
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantt, 47040, Pakistan
| | - Jamal Hussain Shah
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantt, 47040, Pakistan
| | - Seifedine Kadry
- Department of Applied Data Science, Noroff University College, Kristiansand, Norway
- Artificial Intelligence Research Center (AIRC), Ajman University, Ajman, 346, United Arab Emirates
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon
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21
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Abdar M, Salari S, Qahremani S, Lam HK, Karray F, Hussain S, Khosravi A, Acharya UR, Makarenkov V, Nahavandi S. UncertaintyFuseNet: Robust uncertainty-aware hierarchical feature fusion model with Ensemble Monte Carlo Dropout for COVID-19 detection. AN INTERNATIONAL JOURNAL ON INFORMATION FUSION 2023; 90:364-381. [PMID: 36217534 PMCID: PMC9534540 DOI: 10.1016/j.inffus.2022.09.023] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 09/23/2022] [Accepted: 09/25/2022] [Indexed: 05/03/2023]
Abstract
The COVID-19 (Coronavirus disease 2019) pandemic has become a major global threat to human health and well-being. Thus, the development of computer-aided detection (CAD) systems that are capable of accurately distinguishing COVID-19 from other diseases using chest computed tomography (CT) and X-ray data is of immediate priority. Such automatic systems are usually based on traditional machine learning or deep learning methods. Differently from most of the existing studies, which used either CT scan or X-ray images in COVID-19-case classification, we present a new, simple but efficient deep learning feature fusion model, called U n c e r t a i n t y F u s e N e t , which is able to classify accurately large datasets of both of these types of images. We argue that the uncertainty of the model's predictions should be taken into account in the learning process, even though most of the existing studies have overlooked it. We quantify the prediction uncertainty in our feature fusion model using effective Ensemble Monte Carlo Dropout (EMCD) technique. A comprehensive simulation study has been conducted to compare the results of our new model to the existing approaches, evaluating the performance of competing models in terms of Precision, Recall, F-Measure, Accuracy and ROC curves. The obtained results prove the efficiency of our model which provided the prediction accuracy of 99.08% and 96.35% for the considered CT scan and X-ray datasets, respectively. Moreover, our U n c e r t a i n t y F u s e N e t model was generally robust to noise and performed well with previously unseen data. The source code of our implementation is freely available at: https://github.com/moloud1987/UncertaintyFuseNet-for-COVID-19-Classification.
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Affiliation(s)
- Moloud Abdar
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, Australia
| | - Soorena Salari
- Department of Computer Science and Software Engineering, Concordia University, Montreal, Canada
| | - Sina Qahremani
- Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran
| | - Hak-Keung Lam
- Centre for Robotics Research, Department of Engineering, King's College London, London, United Kingdom
| | - Fakhri Karray
- Centre for Pattern Analysis and Machine Intelligence, Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada
- Department of Machine Learning, Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates
| | - Sadiq Hussain
- System Administrator, Dibrugarh University, Dibrugarh, India
| | - Abbas Khosravi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, Australia
| | - U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Clementi, Singapore
- Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore
- Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan
| | - Vladimir Makarenkov
- Department of Computer Science, University of Quebec in Montreal, Montreal, Canada
| | - Saeid Nahavandi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, Australia
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22
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Ghashghaei S, Wood DA, Sadatshojaei E, Jalilpoor M. Grayscale Image Statistical Attributes Effectively Distinguish the Severity of Lung Abnormalities in CT Scan Slices of COVID-19 Patients. SN COMPUTER SCIENCE 2023; 4:201. [PMID: 36789248 PMCID: PMC9912234 DOI: 10.1007/s42979-022-01642-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 12/27/2022] [Indexed: 02/12/2023]
Abstract
Grayscale statistical attributes analysed for 513 extract images taken from pulmonary computed tomography (CT) scan slices of 57 individuals (49 confirmed COVID-19 positive; eight confirmed COVID-19 negative) are able to accurately predict a visual score (VS from 0 to 4) used by a clinician to assess the severity of lung abnormalities in the patients. Some of these attributes can be used graphically to distinguish useful but overlapping distributions for the VS classes. Using machine and deep learning (ML/DL) algorithms with twelve grayscale image attributes as inputs enables the VS classes to be accurately distinguished. A convolutional neural network achieves this with better than 96% accuracy (only 18 images misclassified out of 513) on a supervised learning basis. Analysis of confusion matrices enables the VS prediction performance of ML/DL algorithms to be explored in detail. Those matrices demonstrate that the best performing ML/DL algorithms successfully distinguish between VS classes 0 and 1, which clinicians cannot readily do with the naked eye. Just five image grayscale attributes can also be used to generate an algorithmically defined scoring system (AS) that can also graphically distinguish the degree of pulmonary impacts in the dataset evaluated. The AS classification illustrated involves less overlap between its classes than the VS system and could be exploited as an automated expert system. The best-performing ML/DL models are able to predict the AS classes with better than 99% accuracy using twelve grayscale attributes as inputs. The decision tree and random forest algorithms accomplish that distinction with just one classification error in the 513 images tested.
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Affiliation(s)
- Sara Ghashghaei
- Medical School, Shiraz University of Medical Sciences, Shiraz, Iran
| | | | - Erfan Sadatshojaei
- Department of Chemical Engineering, Shiraz University, Shiraz, 71345 Iran
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23
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Edrees Almalki Y, Zaffar M, Irfan M, Ali Abbas M, Khalid M, Quraishi K, Ali T, Alshehri F, Khalid Alduraibi S, A. Asiri A, Abd Alkhalik Basha M, Alduraibi A, Saeed M, Rahman S. A Novel-based Swin Transfer Based Diagnosis of COVID-19 Patients. INTELLIGENT AUTOMATION & SOFT COMPUTING 2023; 35:163-180. [DOI: 10.32604/iasc.2023.025580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
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24
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Preliminary Stages for COVID-19 Detection Using Image Processing. Diagnostics (Basel) 2022; 12:diagnostics12123171. [PMID: 36553177 PMCID: PMC9777505 DOI: 10.3390/diagnostics12123171] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 11/30/2022] [Accepted: 12/08/2022] [Indexed: 12/23/2022] Open
Abstract
COVID-19 was first discovered in December 2019 in Wuhan. There have been reports of thousands of illnesses and hundreds of deaths in almost every region of the world. Medical images, when combined with cutting-edge technology such as artificial intelligence, have the potential to improve the efficiency of the public health system and deliver faster and more reliable findings in the detection of COVID-19. The process of developing the COVID-19 diagnostic system begins with image accusation and proceeds via preprocessing, feature extraction, and classification. According to literature review, several attempts to develop taxonomies for COVID-19 detection using image processing methods have been introduced. However, most of these adhere to a standard category that exclusively considers classification methods. Therefore, in this study a new taxonomy for the early stages of COVID-19 detection is proposed. It attempts to offer a full grasp of image processing in COVID-19 while considering all phases required prior to classification. The survey concludes with a discussion of outstanding concerns and future directions.
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25
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Mavrogiorgou A, Kiourtis A, Kleftakis S, Mavrogiorgos K, Zafeiropoulos N, Kyriazis D. A Catalogue of Machine Learning Algorithms for Healthcare Risk Predictions. SENSORS (BASEL, SWITZERLAND) 2022; 22:8615. [PMID: 36433212 PMCID: PMC9695983 DOI: 10.3390/s22228615] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Revised: 11/04/2022] [Accepted: 11/04/2022] [Indexed: 05/27/2023]
Abstract
Extracting useful knowledge from proper data analysis is a very challenging task for efficient and timely decision-making. To achieve this, there exist a plethora of machine learning (ML) algorithms, while, especially in healthcare, this complexity increases due to the domain's requirements for analytics-based risk predictions. This manuscript proposes a data analysis mechanism experimented in diverse healthcare scenarios, towards constructing a catalogue of the most efficient ML algorithms to be used depending on the healthcare scenario's requirements and datasets, for efficiently predicting the onset of a disease. To this context, seven (7) different ML algorithms (Naïve Bayes, K-Nearest Neighbors, Decision Tree, Logistic Regression, Random Forest, Neural Networks, Stochastic Gradient Descent) have been executed on top of diverse healthcare scenarios (stroke, COVID-19, diabetes, breast cancer, kidney disease, heart failure). Based on a variety of performance metrics (accuracy, recall, precision, F1-score, specificity, confusion matrix), it has been identified that a sub-set of ML algorithms are more efficient for timely predictions under specific healthcare scenarios, and that is why the envisioned ML catalogue prioritizes the ML algorithms to be used, depending on the scenarios' nature and needed metrics. Further evaluation must be performed considering additional scenarios, involving state-of-the-art techniques (e.g., cloud deployment, federated ML) for improving the mechanism's efficiency.
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Affiliation(s)
- Argyro Mavrogiorgou
- Department of Digital Systems, University of Piraeus, 185 34 Piraeus, Greece
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26
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Hamza A, Attique Khan M, Wang SH, Alhaisoni M, Alharbi M, Hussein HS, Alshazly H, Kim YJ, Cha J. COVID-19 classification using chest X-ray images based on fusion-assisted deep Bayesian optimization and Grad-CAM visualization. Front Public Health 2022; 10:1046296. [PMID: 36408000 PMCID: PMC9672507 DOI: 10.3389/fpubh.2022.1046296] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Accepted: 10/12/2022] [Indexed: 11/06/2022] Open
Abstract
The COVID-19 virus's rapid global spread has caused millions of illnesses and deaths. As a result, it has disastrous consequences for people's lives, public health, and the global economy. Clinical studies have revealed a link between the severity of COVID-19 cases and the amount of virus present in infected people's lungs. Imaging techniques such as computed tomography (CT) and chest x-rays can detect COVID-19 (CXR). Manual inspection of these images is a difficult process, so computerized techniques are widely used. Deep convolutional neural networks (DCNNs) are a type of machine learning that is frequently used in computer vision applications, particularly in medical imaging, to detect and classify infected regions. These techniques can assist medical personnel in the detection of patients with COVID-19. In this article, a Bayesian optimized DCNN and explainable AI-based framework is proposed for the classification of COVID-19 from the chest X-ray images. The proposed method starts with a multi-filter contrast enhancement technique that increases the visibility of the infected part. Two pre-trained deep models, namely, EfficientNet-B0 and MobileNet-V2, are fine-tuned according to the target classes and then trained by employing Bayesian optimization (BO). Through BO, hyperparameters have been selected instead of static initialization. Features are extracted from the trained model and fused using a slicing-based serial fusion approach. The fused features are classified using machine learning classifiers for the final classification. Moreover, visualization is performed using a Grad-CAM that highlights the infected part in the image. Three publically available COVID-19 datasets are used for the experimental process to obtain improved accuracies of 98.8, 97.9, and 99.4%, respectively.
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Affiliation(s)
- Ameer Hamza
- Department of Computer Science, HITEC University, Taxila, Pakistan
| | - Muhammad Attique Khan
- Department of Computer Science, HITEC University, Taxila, Pakistan,*Correspondence: Muhammad Attique Khan
| | - Shui-Hua Wang
- Department of Mathematics, University of Leicester, Leicester, United Kingdom
| | - Majed Alhaisoni
- Computer Sciences Department, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Meshal Alharbi
- Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
| | - Hany S. Hussein
- Electrical Engineering Department, College of Engineering, King Khalid University, Abha, Saudi Arabia,Electrical Engineering Department, Faculty of Engineering, Aswan University, Aswan, Egypt
| | - Hammam Alshazly
- Faculty of Computers and Information, South Valley University, Qena, Egypt
| | - Ye Jin Kim
- Department of Computer Science, Hanyang University, Seoul, South Korea
| | - Jaehyuk Cha
- Department of Computer Science, Hanyang University, Seoul, South Korea,Jaehyuk Cha
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27
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Cao K, Deng T, Zhang C, Lu L, Li L. A CNN-transformer fusion network for COVID-19 CXR image classification. PLoS One 2022; 17:e0276758. [PMID: 36301907 PMCID: PMC9612494 DOI: 10.1371/journal.pone.0276758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 10/12/2022] [Indexed: 11/04/2022] Open
Abstract
The global health crisis due to the fast spread of coronavirus disease (Covid-19) has caused great danger to all aspects of healthcare, economy, and other aspects. The highly infectious and insidious nature of the new coronavirus greatly increases the difficulty of outbreak prevention and control. The early and rapid detection of Covid-19 is an effective way to reduce the spread of Covid-19. However, detecting Covid-19 accurately and quickly in large populations remains to be a major challenge worldwide. In this study, A CNN-transformer fusion framework is proposed for the automatic classification of pneumonia on chest X-ray. This framework includes two parts: data processing and image classification. The data processing stage is to eliminate the differences between data from different medical institutions so that they have the same storage format; in the image classification stage, we use a multi-branch network with a custom convolution module and a transformer module, including feature extraction, feature focus, and feature classification sub-networks. Feature extraction subnetworks extract the shallow features of the image and interact with the information through the convolution and transformer modules. Both the local and global features are extracted by the convolution module and transformer module of feature-focus subnetworks, and are classified by the feature classification subnetworks. The proposed network could decide whether or not a patient has pneumonia, and differentiate between Covid-19 and bacterial pneumonia. This network was implemented on the collected benchmark datasets and the result shows that accuracy, precision, recall, and F1 score are 97.09%, 97.16%, 96.93%, and 97.04%, respectively. Our network was compared with other researchers' proposed methods and achieved better results in terms of accuracy, precision, and F1 score, proving that it is superior for Covid-19 detection. With further improvements to this network, we hope that it will provide doctors with an effective tool for diagnosing Covid-19.
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Affiliation(s)
- Kai Cao
- Key Laboratory of China’s Ethnic Languages and Information Technology of Ministry of Education, Northwest Minzu University, Lanzhou, Gansu, China
| | - Tao Deng
- School of Mathematics and Computer Science, Northwest Minzu University, Lanzhou, Gansu, China
- Key Laboratory of Streaming Data Computing Technologies and Application, Northwest Minzu University, Lanzhou, Gansu, China
| | - Chuanlin Zhang
- School of Mathematics and Computer Science, Northwest Minzu University, Lanzhou, Gansu, China
| | - Limeng Lu
- Key Laboratory of China’s Ethnic Languages and Information Technology of Ministry of Education, Northwest Minzu University, Lanzhou, Gansu, China
| | - Lin Li
- School of Computing, University of Leeds, Leeds, United Kingdom
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28
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Visuña L, Yang D, Garcia-Blas J, Carretero J. Computer-aided diagnostic for classifying chest X-ray images using deep ensemble learning. BMC Med Imaging 2022; 22:178. [PMID: 36243705 PMCID: PMC9568999 DOI: 10.1186/s12880-022-00904-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 09/05/2022] [Indexed: 11/16/2022] Open
Abstract
Background Nowadays doctors and radiologists are overwhelmed with a huge amount of work. This led to the effort to design different Computer-Aided Diagnosis systems (CAD system), with the aim of accomplishing a faster and more accurate diagnosis. The current development of deep learning is a big opportunity for the development of new CADs. In this paper, we propose a novel architecture for a convolutional neural network (CNN) ensemble for classifying chest X-ray (CRX) images into four classes: viral Pneumonia, Tuberculosis, COVID-19, and Healthy. Although Computed tomography (CT) is the best way to detect and diagnoses pulmonary issues, CT is more expensive than CRX. Furthermore, CRX is commonly the first step in the diagnosis, so it’s very important to be accurate in the early stages of diagnosis and treatment. Results We applied the transfer learning technique and data augmentation to all CNNs for obtaining better performance. We have designed and evaluated two different CNN-ensembles: Stacking and Voting. This system is ready to be applied in a CAD system to automated diagnosis such a second or previous opinion before the doctors or radiology’s. Our results show a great improvement, 99% accuracy of the Stacking Ensemble and 98% of accuracy for the the Voting Ensemble. Conclusions To minimize missclassifications, we included six different base CNN models in our architecture (VGG16, VGG19, InceptionV3, ResNet101V2, DenseNet121 and CheXnet) and it could be extended to any number as well as we expect extend the number of diseases to detected. The proposed method has been validated using a large dataset created by mixing several public datasets with different image sizes and quality. As we demonstrate in the evaluation carried out, we reach better results and generalization compared with previous works. In addition, we make a first approach to explainable deep learning with the objective of providing professionals more information that may be valuable when evaluating CRXs.
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Affiliation(s)
- Lara Visuña
- Department of Computer Science and Engineering, University Carlos III, Madrid, Spain
| | - Dandi Yang
- Beijing Electro-Mechanical Engineering Institute, Beijing, China
| | - Javier Garcia-Blas
- Department of Computer Science and Engineering, University Carlos III, Madrid, Spain
| | - Jesus Carretero
- Department of Computer Science and Engineering, University Carlos III, Madrid, Spain.
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29
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Trivedi A, Robinson C, Blazes M, Ortiz A, Desbiens J, Gupta S, Dodhia R, Bhatraju PK, Liles WC, Kalpathy-Cramer J, Lee AY, Lavista Ferres JM. Deep learning models for COVID-19 chest x-ray classification: Preventing shortcut learning using feature disentanglement. PLoS One 2022; 17:e0274098. [PMID: 36201483 PMCID: PMC9536609 DOI: 10.1371/journal.pone.0274098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 08/22/2022] [Indexed: 11/07/2022] Open
Abstract
In response to the COVID-19 global pandemic, recent research has proposed creating deep learning based models that use chest radiographs (CXRs) in a variety of clinical tasks to help manage the crisis. However, the size of existing datasets of CXRs from COVID-19+ patients are relatively small, and researchers often pool CXR data from multiple sources, for example, using different x-ray machines in various patient populations under different clinical scenarios. Deep learning models trained on such datasets have been shown to overfit to erroneous features instead of learning pulmonary characteristics in a phenomenon known as shortcut learning. We propose adding feature disentanglement to the training process. This technique forces the models to identify pulmonary features from the images and penalizes them for learning features that can discriminate between the original datasets that the images come from. We find that models trained in this way indeed have better generalization performance on unseen data; in the best case we found that it improved AUC by 0.13 on held out data. We further find that this outperforms masking out non-lung parts of the CXRs and performing histogram equalization, both of which are recently proposed methods for removing biases in CXR datasets.
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Affiliation(s)
- Anusua Trivedi
- AI for Good Research Lab, Microsoft, Redmond, WA, United States of America
- Flipkart US R&D, Seattle, WA, United States of America
| | - Caleb Robinson
- AI for Good Research Lab, Microsoft, Redmond, WA, United States of America
| | - Marian Blazes
- Department of Ophthalmology, University of Washington, Seattle, WA, United States of America
| | - Anthony Ortiz
- AI for Good Research Lab, Microsoft, Redmond, WA, United States of America
| | - Jocelyn Desbiens
- Intelligent Retinal Imaging Systems, Pensacola, FL, United States of America
| | - Sunil Gupta
- Intelligent Retinal Imaging Systems, Pensacola, FL, United States of America
| | - Rahul Dodhia
- AI for Good Research Lab, Microsoft, Redmond, WA, United States of America
| | - Pavan K. Bhatraju
- Department of Medicine and Sepsis Center of Research Excellence, University of Washington (SCORE-UW), Seattle, WA, United States of America
- Division of Pulmonary, Critical Care and Sleep Medicine, University of Washington, Seattle, WA, United States of America
| | - W. Conrad Liles
- Department of Medicine and Sepsis Center of Research Excellence, University of Washington (SCORE-UW), Seattle, WA, United States of America
| | | | - Aaron Y. Lee
- Department of Ophthalmology, University of Washington, Seattle, WA, United States of America
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30
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Suganya G, Premalatha M, Geetha S, Chowdary GJ, Kadry S. Detection of COVID-19 Cases from Chest X-Rays using Deep Learning Feature Extractor and Multilevel Voting Classifier. INT J UNCERTAIN FUZZ 2022. [DOI: 10.1142/s0218488522500222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Purpose: During the current pandemic scientists, researchers, and health professionals across the globe are in search of new technological methods for tackling COVID-19. The magnificent performance reported by machine learning and deep learning methods in the previous epidemic has encouraged researchers to develop systems with these methods to diagnose COVID-19. Methods: In this paper, an ensemble-based multi-level voting model is proposed to diagnose COVID-19 from chest x-rays. The multi-level voting model proposed in this paper is built using four machine learning algorithms namely Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM) with a linear kernel, and K-Nearest Neighbor (KNN). These algorithms are trained with features extracted using the ResNet50 deep learning model before merging them to form the voting model. In this work, voting is performed at two levels, at level 1 these four algorithms are grouped into 2 sets consisting of two algorithms each (set 1 — SVM with linear kernel and LR and set 2 — RF and KNN) and intra set hard voting is performed. At level 2 these two sets are merged using hard voting to form the proposed model. Results: The proposed multilevel voting model outperformed all the machine learning algorithms, pre-trained models, and other proposed works with an accuracy of 100% and specificity of 100%. Conclusion: The proposed model helps for the faster diagnosis of COVID-19 across the globe.
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Affiliation(s)
- G. Suganya
- School of Computer Science and Engineering, Vellore Institute of Technology Chennai Campus, India
| | - M. Premalatha
- School of Computer Science and Engineering, Vellore Institute of Technology Chennai Campus, India
| | - S. Geetha
- School of Computer Science and Engineering, Vellore Institute of Technology Chennai Campus, India
| | - G. Jignesh Chowdary
- School of Computer Science and Engineering, Vellore Institute of Technology Chennai Campus, India
| | - Seifedine Kadry
- Department of Applied Data Science, Noroff University College, Kristiansand, Norway
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon
- Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman, United Arab Emirates
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31
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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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32
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Comprehensive Survey of Machine Learning Systems for COVID-19 Detection. J Imaging 2022; 8:jimaging8100267. [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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Costa YMG, Silva SA, Teixeira LO, Pereira RM, Bertolini D, Britto AS, Oliveira LS, Cavalcanti GDC. COVID-19 Detection on Chest X-ray and CT Scan: A Review of the Top-100 Most Cited Papers. SENSORS (BASEL, SWITZERLAND) 2022; 22:7303. [PMID: 36236402 PMCID: PMC9570662 DOI: 10.3390/s22197303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 09/13/2022] [Accepted: 09/19/2022] [Indexed: 06/16/2023]
Abstract
Since the beginning of the COVID-19 pandemic, many works have been published proposing solutions to the problems that arose in this scenario. In this vein, one of the topics that attracted the most attention is the development of computer-based strategies to detect COVID-19 from thoracic medical imaging, such as chest X-ray (CXR) and computerized tomography scan (CT scan). By searching for works already published on this theme, we can easily find thousands of them. This is partly explained by the fact that the most severe worldwide pandemic emerged amid the technological advances recently achieved, and also considering the technical facilities to deal with the large amount of data produced in this context. Even though several of these works describe important advances, we cannot overlook the fact that others only use well-known methods and techniques without a more relevant and critical contribution. Hence, differentiating the works with the most relevant contributions is not a trivial task. The number of citations obtained by a paper is probably the most straightforward and intuitive way to verify its impact on the research community. Aiming to help researchers in this scenario, we present a review of the top-100 most cited papers in this field of investigation according to the Google Scholar search engine. We evaluate the distribution of the top-100 papers taking into account some important aspects, such as the type of medical imaging explored, learning settings, segmentation strategy, explainable artificial intelligence (XAI), and finally, the dataset and code availability.
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Affiliation(s)
- Yandre M. G. Costa
- Departamento de Informática, Universidade Estadual de Maringá, Maringá 87020-900, Brazil
| | - Sergio A. Silva
- Departamento de Informática, Universidade Estadual de Maringá, Maringá 87020-900, Brazil
| | - Lucas O. Teixeira
- Departamento de Informática, Universidade Estadual de Maringá, Maringá 87020-900, Brazil
| | | | - Diego Bertolini
- Departamento Acadêmico de Ciência da Computação, Universidade Tecnológica Federal do Paraná, Campo Mourão 87301-899, Brazil
| | - Alceu S. Britto
- Departmento de Ciência da Computação, Pontifícia Universidade Católica do Paraná, Curitiba 80215-901, Brazil
| | - Luiz S. Oliveira
- Departamento de Informática, Universidade Federal do Paraná, Curitiba 81531-980, Brazil
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Jalali SMJ, Ahmadian M, Ahmadian S, Hedjam R, Khosravi A, Nahavandi S. X-ray image based COVID-19 detection using evolutionary deep learning approach. EXPERT SYSTEMS WITH APPLICATIONS 2022; 201:116942. [PMID: 35378906 PMCID: PMC8966159 DOI: 10.1016/j.eswa.2022.116942] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2020] [Revised: 12/24/2021] [Accepted: 03/17/2022] [Indexed: 06/01/2023]
Abstract
Radiological methodologies, such as chest x-rays and CT, are widely employed to help diagnose and monitor COVID-19 disease. COVID-19 displays certain radiological patterns easily detectable by X-rays of the chest. Therefore, radiologists can investigate these patterns for detecting coronavirus disease. However, this task is time-consuming and needs lots of trial and error. One of the main solutions to resolve this issue is to apply intelligent techniques such as deep learning (DL) models to automatically analyze the chest X-rays. Nevertheless, fine-tuning of architecture and hyperparameters of DL models is a complex and time-consuming procedure. In this paper, we propose an effective method to detect COVID-19 disease by applying convolutional neural network (CNN) to the chest X-ray images. To improve the accuracy of the proposed method, the last Softmax CNN layer is replaced with a K -nearest neighbors (KNN) classifier which takes into account the agreement of the neighborhood labeling. Moreover, we develop a novel evolutionary algorithm by improving the basic version of competitive swarm optimizer. To this end, three powerful evolutionary operators: Cauchy Mutation (CM), Evolutionary Boundary Constraint Handling (EBCH), and tent chaotic map are incorporated into the search process of the proposed evolutionary algorithm to speed up its convergence and make an excellent balance between exploration and exploitation phases. Then, the proposed evolutionary algorithm is used to automatically achieve the optimal values of CNN's hyperparameters leading to a significant improvement in the classification accuracy of the proposed method. Comprehensive comparative results reveal that compared with current models in the literature, the proposed method performs significantly more efficient.
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Affiliation(s)
| | - Milad Ahmadian
- Department of Computer Engineering, Razi University, Kermanshah, Iran
| | - Sajad Ahmadian
- Faculty of Information Technology, Kermanshah University of Technology, Kermanshah, Iran
| | - Rachid Hedjam
- Department of Computer science, Sultan Qaboos University, Muscat, Sultanate of Oman
| | - Abbas Khosravi
- Institute for Intelligent Systems Research and Innovation, (IISRI), Deakin University, Geelong, Australia
| | - Saeid Nahavandi
- Institute for Intelligent Systems Research and Innovation, (IISRI), Deakin University, Geelong, Australia
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Siddiqui S, Arifeen M, Hopgood A, Good A, Gegov A, Hossain E, Rahman W, Hossain S, Al Jannat S, Ferdous R, Masum S. Deep Learning Models for the Diagnosis and Screening of COVID-19: A Systematic Review. SN COMPUTER SCIENCE 2022; 3:397. [PMID: 35911439 PMCID: PMC9312319 DOI: 10.1007/s42979-022-01326-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 04/11/2022] [Indexed: 10/29/2022]
Abstract
COVID-19, caused by SARS-CoV-2, has been declared as a global pandemic by WHO. Early diagnosis of COVID-19 patients may reduce the impact of coronavirus using modern computational methods like deep learning. Various deep learning models based on CT and chest X-ray images are studied and compared in this study as an alternative solution to reverse transcription-polymerase chain reactions. This study consists of three stages: planning, conduction, and analysis/reporting. In the conduction stage, inclusion and exclusion criteria are applied to the literature searching and identification. Then, we have implemented quality assessment rules, where over 75 scored articles in the literature were included. Finally, in the analysis/reporting stage, all the papers are reviewed and analysed. After the quality assessment of the individual papers, this study adopted 57 articles for the systematic literature review. From these reviews, the critical analysis of each paper, including the represented matrix for the model evaluation, existing contributions, and motivation, has been tracked with suitable illustrations. We have also interpreted several insights of each paper with appropriate annotation. Further, a set of comparisons has been enumerated with suitable discussion. Convolutional neural networks are the most commonly used deep learning architecture for COVID-19 disease classification and identification from X-ray and CT images. Various prior studies did not include data from a hospital setting nor did they consider data preprocessing before training a deep learning model.
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Affiliation(s)
- Shah Siddiqui
- Faculty of Technology, The University of Portsmouth (UoP), Portland Building, Portland Street, Portsmouth, PO1 3AH UK.,School of Computing, University of Portsmouth (UoP), Lion Terrace, Portsmouth, PO1 3HE UK
| | - Murshedul Arifeen
- Time Research and Innovation (TRI), 189 Foundry Lane, Southampton, SO15 3JZ UK.,336/7, TV Road East Rampura, Khilgaon, Dhaka 1219 Bangladesh
| | - Adrian Hopgood
- Faculty of Technology, The University of Portsmouth (UoP), Portland Building, Portland Street, Portsmouth, PO1 3AH UK
| | - Alice Good
- Faculty of Technology, The University of Portsmouth (UoP), Portland Building, Portland Street, Portsmouth, PO1 3AH UK
| | - Alexander Gegov
- Faculty of Technology, The University of Portsmouth (UoP), Portland Building, Portland Street, Portsmouth, PO1 3AH UK
| | - Elias Hossain
- Time Research and Innovation (TRI), 189 Foundry Lane, Southampton, SO15 3JZ UK.,336/7, TV Road East Rampura, Khilgaon, Dhaka 1219 Bangladesh
| | - Wahidur Rahman
- Time Research and Innovation (TRI), 189 Foundry Lane, Southampton, SO15 3JZ UK.,336/7, TV Road East Rampura, Khilgaon, Dhaka 1219 Bangladesh
| | - Shazzad Hossain
- Time Research and Innovation (TRI), 189 Foundry Lane, Southampton, SO15 3JZ UK.,336/7, TV Road East Rampura, Khilgaon, Dhaka 1219 Bangladesh
| | - Sabila Al Jannat
- Time Research and Innovation (TRI), 189 Foundry Lane, Southampton, SO15 3JZ UK.,336/7, TV Road East Rampura, Khilgaon, Dhaka 1219 Bangladesh
| | - Rezowan Ferdous
- Time Research and Innovation (TRI), 189 Foundry Lane, Southampton, SO15 3JZ UK.,336/7, TV Road East Rampura, Khilgaon, Dhaka 1219 Bangladesh
| | - Shamsul Masum
- Faculty of Technology, The University of Portsmouth (UoP), Portland Building, Portland Street, Portsmouth, PO1 3AH UK
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Gopatoti A, Vijayalakshmi P. CXGNet: A tri-phase chest X-ray image classification for COVID-19 diagnosis using deep CNN with enhanced grey-wolf optimizer. Biomed Signal Process Control 2022; 77:103860. [PMID: 35692695 PMCID: PMC9167923 DOI: 10.1016/j.bspc.2022.103860] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Revised: 05/17/2022] [Accepted: 06/04/2022] [Indexed: 11/15/2022]
Abstract
The coronavirus disease 2019 (COVID-19) epidemic had a significant impact on daily life in many nations and global public health. COVID's quick spread has become one of the biggest disruptive calamities in the world. In the fight against COVID-19, it's critical to keep a close eye on the initial stage of infection in patients. Furthermore, early COVID-19 discovery by precise diagnosis, especially in patients with no evident symptoms, may reduce the patient's death rate and can stop the spread of COVID-19. When compared to CT images, chest X-ray (CXR) images are now widely employed for COVID-19 diagnosis since CXR images contain more robust features of the lung. Furthermore, radiologists can easily diagnose CXR images because of its operating speed and low cost, and it is promising for emergency situations and therapy. This work proposes a tri-stage CXR image based COVID-19 classification model using deep learning convolutional neural networks (DLCNN) with an optimal feature selection technique named as enhanced grey-wolf optimizer with genetic algorithm (EGWO-GA), which is denoted as CXGNet. The proposed CXGNet is implemented as multiple classes, such as 4-class, 3-class, and 2-class models based on the diseases. Extensive simulation outcome discloses the superiority of the proposed CXGNet model with enhanced classification accuracy of 94.00% for the 4-class model, 97.05% of accuracy for the 3-class model, and 100% accuracy for the 2-class model as compared to conventional methods.
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Affiliation(s)
- Anandbabu Gopatoti
- Department of Electronics and Communication Engineering, Hindusthan College of Engineering and Technology, Coimbatore, Tamil Nadu, India
- Anna University, Chennai, Tamil Nadu, India
| | - P Vijayalakshmi
- Department of Electronics and Communication Engineering, Hindusthan College of Engineering and Technology, Coimbatore, Tamil Nadu, India
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Sasikaladevi N. Delaunay triangulation based intelligent system for the diagnosis of covid from the low radiation CXR images. JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2022; 14:1-10. [PMID: 37360780 PMCID: PMC10112999 DOI: 10.1007/s12652-022-04329-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Accepted: 07/11/2022] [Indexed: 06/28/2023]
Abstract
Covid-19 is a viral infection that causes a profound impact on the lives of the World population. It is a global pandemic spreading across the world in a faster way. It made a global impact on the health, economy, and education system in all the countries. As it is a rapidly spreading disease, prevention demands a fast and accurate diagnosis system. In a highly densely populated country, the demand for fast and affordable early diagnosis is required to reduce the disaster. Within this diagnosis time, the infection spreads rapidly and worsens the infected person's status. To provide a faster and more affordable early diagnosis of covid, posterior-anterior chest radiographs (CXR) are used. Diagnosis of covid from CXR is challenging due to the images' interclass similarity and intraclass variation. This study proposes a deep learning-based robust early diagnosis method for covid. To balance the intraclass variation and interclass similarity in CXR images, the deep fused Delaunay triangulation (DT) is proposed as the CXR has low radiation and unbalanced quality images. The deep features are to be extracted to increase the robustness of the diagnosis method. Without segmentation, the proposed DT algorithm achieves the accurate visualization of the suspicious region in the CXR. The proposed model is trained and tested by the largest benchmark covid-19 radiology dataset with 3616 covid CXR images and 3500 standard CXR images. The performance of the proposed system is analyzed in terms of accuracy, sensitivity, specificity, and AUC. The proposed system yields the highest validation accuracy.
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Affiliation(s)
- N. Sasikaladevi
- Dept. of CSE, School of Computing, SASTRA Deemed University, Thanjavur, TN India
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A three-stage ensemble boosted convolutional neural network for classification and analysis of COVID-19 chest x-ray images. INTERNATIONAL JOURNAL OF COGNITIVE COMPUTING IN ENGINEERING 2022; 3:35-45. [PMCID: PMC8802500 DOI: 10.1016/j.ijcce.2022.01.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/24/2021] [Revised: 01/30/2022] [Accepted: 01/30/2022] [Indexed: 06/01/2023]
Abstract
For the identification and classification of COVID-19, this research presents a three-stage ensemble boosted convolutional neural network model. A conventional segmentation model (ResUNet) is used to increase the model's performance in the initial step of processing the CXR datasets. In the second step, the CNN is used to extract the features from the pictures in the training dataset using machine learning techniques. Using machine learning (ML) techniques, the retrieved characteristics are then combined by voting in the third stage. There are 5178 aberrant CXR photos and 4310 normal CXR images used in this investigation. Models like CNN and ML can't compete with the suggested model. 99.35% of the model's measurements are accurate and precise, and 98% of its recall and F1-score are perfect. It is argued that the suggested model provides a rigorous and trustworthy evaluation of clinical decision-making in the setting of a public health crisis.
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Ghashghaei S, Wood DA, Sadatshojaei E, Jalilpoor M. Grayscale image statistics of COVID‐19 patient CT scans characterize lung condition with machine and deep learning. Chronic Dis Transl Med 2022; 8:191-206. [PMID: 35942198 PMCID: PMC9347876 DOI: 10.1002/cdt3.27] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 03/28/2022] [Accepted: 04/08/2022] [Indexed: 11/30/2022] Open
Abstract
Background Grayscale image attributes of computed tomography (CT) of pulmonary scans contain valuable information relating to patients with respiratory ailments. These attributes are used to evaluate the severity of lung conditions of patients confirmed to be with and without COVID‐19. Method Five hundred thirteen CT images relating to 57 patients (49 with COVID‐19; 8 free of COVID‐19) were collected at Namazi Medical Centre (Shiraz, Iran) in 2020 and 2021. Five visual scores (VS: 0, 1, 2, 3, or 4) are clinically assigned to these images with the score increasing with the severity of COVID‐19‐related lung conditions. Eleven deep learning and machine learning techniques (DL/ML) are used to distinguish the VS class based on 12 grayscale image attributes. Results The convolutional neural network achieves 96.49% VS accuracy (18 errors from 513 images) successfully distinguishing VS Classes 0 and 1, outperforming clinicians’ visual inspections. An algorithmic score (AS), involving just five grayscale image attributes, is developed independently of clinicians’ assessments (99.81% AS accuracy; 1 error from 513 images). Conclusion Grayscale CT image attributes can be successfully used to distinguish the severity of COVID‐19 lung damage. The AS technique developed provides a suitable basis for an automated system using ML/DL methods and 12 image attributes. Grayscale image statistics of CT scans can effectively classify lung abnormalities Graphical trends of grayscale statistics distinguish visual assessments COVID‐19 classes Machine/deep learning algorithms predict severity from image grayscale attributes Algorithmic class systems can be established using just five grayscale attributes Confusion matrices provide detailed insight to algorithm prediction capabilities
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Affiliation(s)
- Sara Ghashghaei
- Medical School Shiraz University of Medical Sciences Shiraz Iran
| | - David A. Wood
- Department of Research DWA Energy Limited Lincoln LN5 9JP UK
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40
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Li X. Design of Online Ideological and Political Teaching of Building Architecture from the Perspective of Machine Learning. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:8347235. [PMID: 35634071 PMCID: PMC9132646 DOI: 10.1155/2022/8347235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 03/31/2022] [Indexed: 11/18/2022]
Abstract
Under the background of the rapid progress of machine learning (ML) and information technology, the traditional classroom model is gradually replaced by the self-media classroom. This thesis aims to solve the problems of understanding abstract content and limited teaching in practical teaching. Based on the ideological and political integration teaching of building architecture, the online classroom design principle reflects the classroom optimization and design principle of building architecture from the perspective of ML. The data advantage is utilized, and the classroom application model is established using the deep learning algorithm through information input. The scene of field survey and building construction is presented from the perspective of mechanization. The simulated results are displayed in the classroom through online teaching combined with ideological education and political teaching mode. The research shows that ML combined with online classroom design breaks through the book face-to-face teaching mode of the traditional classroom through the dynamic demonstration of actual construction, the virtual building structure, and the innovative design of building material entity. It stimulates students' interest in architecture courses, improves the overall learning efficiency, and promotes the ideological and political integration of building architecture courses. Besides, it is conducive to cultivating comprehensive architectural talents in the new era.
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Affiliation(s)
- Xuhui Li
- Engineering Economics Department, Henan Finance University, Zhengzhou 451464, Henan, China
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41
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Meraihi Y, Gabis AB, Mirjalili S, Ramdane-Cherif A, Alsaadi FE. Machine Learning-Based Research for COVID-19 Detection, Diagnosis, and Prediction: A Survey. SN COMPUTER SCIENCE 2022; 3:286. [PMID: 35578678 PMCID: PMC9096341 DOI: 10.1007/s42979-022-01184-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Accepted: 04/30/2022] [Indexed: 12/12/2022]
Abstract
The year 2020 experienced an unprecedented pandemic called COVID-19, which impacted the whole world. The absence of treatment has motivated research in all fields to deal with it. In Computer Science, contributions mainly include the development of methods for the diagnosis, detection, and prediction of COVID-19 cases. Data science and Machine Learning (ML) are the most widely used techniques in this area. This paper presents an overview of more than 160 ML-based approaches developed to combat COVID-19. They come from various sources like Elsevier, Springer, ArXiv, MedRxiv, and IEEE Xplore. They are analyzed and classified into two categories: Supervised Learning-based approaches and Deep Learning-based ones. In each category, the employed ML algorithm is specified and a number of used parameters is given. The parameters set for each of the algorithms are gathered in different tables. They include the type of the addressed problem (detection, diagnosis, or detection), the type of the analyzed data (Text data, X-ray images, CT images, Time series, Clinical data,...) and the evaluated metrics (accuracy, precision, sensitivity, specificity, F1-Score, and AUC). The study discusses the collected information and provides a number of statistics drawing a picture about the state of the art. Results show that Deep Learning is used in 79% of cases where 65% of them are based on the Convolutional Neural Network (CNN) and 17% use Specialized CNN. On his side, supervised learning is found in only 16% of the reviewed approaches and only Random Forest, Support Vector Machine (SVM) and Regression algorithms are employed.
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Affiliation(s)
- Yassine Meraihi
- LIST Laboratory, University of M'Hamed Bougara Boumerdes, Avenue of Independence, 35000 Boumerdes, Algeria
| | - Asma Benmessaoud Gabis
- Ecole nationale Supérieure d'Informatique, Laboratoire des Méthodes de Conception des Systèmes, BP 68 M, 16309 Oued-Smar, Alger Algeria
| | - Seyedali Mirjalili
- Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Fortitude Valley, Brisbane, QLD 4006 Australia.,Yonsei Frontier Lab, Yonsei University, Seoul, Korea
| | - Amar Ramdane-Cherif
- LISV Laboratory, University of Versailles St-Quentin-en-Yvelines, 10-12 Avenue of Europe, 78140 Velizy, France
| | - Fawaz E Alsaadi
- Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
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Álvarez-Rodríguez L, Moura JD, Novo J, Ortega M. Does imbalance in chest X-ray datasets produce biased deep learning approaches for COVID-19 screening? BMC Med Res Methodol 2022; 22:125. [PMID: 35484483 PMCID: PMC9046709 DOI: 10.1186/s12874-022-01578-w] [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: 10/08/2021] [Accepted: 03/21/2022] [Indexed: 11/10/2022] Open
Abstract
Background The health crisis resulting from the global COVID-19 pandemic highlighted more than ever the need for rapid, reliable and safe methods of diagnosis and monitoring of respiratory diseases. To study pulmonary involvement in detail, one of the most common resources is the use of different lung imaging modalities (like chest radiography) to explore the possible affected areas. Methods The study of patient characteristics like sex and age in pathologies of this type is crucial for gaining knowledge of the disease and for avoiding biases due to the clear scarcity of data when developing representative systems. In this work, we performed an analysis of these factors in chest X-ray images to identify biases. Specifically, 11 imbalance scenarios were defined with female and male COVID-19 patients present in different proportions for the sex analysis, and 6 scenarios where only one specific age range was used for training for the age factor. In each study, 3 different approaches for automatic COVID-19 screening were used: Normal vs COVID-19, Pneumonia vs COVID-19 and Non-COVID-19 vs COVID-19. The study was validated using two public chest X-ray datasets, allowing a reliable analysis to support the clinical decision-making process. Results The results for the sex-related analysis indicate this factor slightly affects the system in the Normal VS COVID-19 and Pneumonia VS COVID-19 approaches, although the identified differences are not relevant enough to worsen considerably the system. Regarding the age-related analysis, this factor was observed to be influencing the system in a more consistent way than the sex factor, as it was present in all considered scenarios. However, this worsening does not represent a major factor, as it is not of great magnitude. Conclusions Multiple studies have been conducted in other fields in order to determine if certain patient characteristics such as sex or age influenced these deep learning systems. However, to the best of our knowledge, this study has not been done for COVID-19 despite the urgency and lack of COVID-19 chest x-ray images. The presented results evidenced that the proposed methodology and tested approaches allow a robust and reliable analysis to support the clinical decision-making process in this pandemic scenario. Supplementary Information The online version contains supplementary material available at (10.1186/s12874-022-01578-w).
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Affiliation(s)
- Lorena Álvarez-Rodríguez
- Centro de Investigación CITIC, Universidade da Coruña, Campus de Elviña, A Coruña, 15071, Spain.,Grupo VARPA, Instituto de Investigación Biomédica de A Coruña (INIBIC), Universidade da Coruña, A Coruña, 15006, Spain
| | - Joaquim de Moura
- Centro de Investigación CITIC, Universidade da Coruña, Campus de Elviña, A Coruña, 15071, Spain. .,Grupo VARPA, Instituto de Investigación Biomédica de A Coruña (INIBIC), Universidade da Coruña, A Coruña, 15006, Spain.
| | - Jorge Novo
- Centro de Investigación CITIC, Universidade da Coruña, Campus de Elviña, A Coruña, 15071, Spain.,Grupo VARPA, Instituto de Investigación Biomédica de A Coruña (INIBIC), Universidade da Coruña, A Coruña, 15006, Spain
| | - Marcos Ortega
- Centro de Investigación CITIC, Universidade da Coruña, Campus de Elviña, A Coruña, 15071, Spain.,Grupo VARPA, Instituto de Investigación Biomédica de A Coruña (INIBIC), Universidade da Coruña, A Coruña, 15006, Spain
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Leveraging deep learning for COVID-19 diagnosis through chest imaging. Neural Comput Appl 2022; 34:14003-14012. [PMID: 35462631 PMCID: PMC9017721 DOI: 10.1007/s00521-022-07250-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Accepted: 03/29/2022] [Indexed: 11/30/2022]
Abstract
COVID-19 has taken a toll on the entire world, rendering serious illness and high mortality rate. In the present day, when the globe is hit by a pandemic, those suspected to be infected by the virus need to confirm its presence to seek immediate medical attention to avoid adverse outcomes and also to prevent further transmission of the virus in their close contacts by ensuring timely isolation. The most reliable laboratory testing currently available is the reverse transcription–polymerase chain reaction (RT-PCR) test. Although the test is considered gold standard, 20–25% of results can still be false negatives, which has lately led physicians to recommend medical imaging in specific cases. Our research examines the aspect of chest imaging as a method to diagnose COVID-19. This work is not directed to establish an alternative to RT-PCR, but to aid physicians in determining the presence of virus in medical images. As the disease presents lung involvement, it provides a basis to explore computer vision for classification in radiographic images. In this paper, authors compare the performance of various models, namely ResNet-50, EfficientNetB0, VGG-16 and a custom convolutional neural network (CNN) for detecting the presence of virus in chest computed tomography (CT) scan and chest X-ray images. The most promising results have been derived by using ResNet-50 on CT scans with an accuracy of 98.9% and ResNet-50 on X-rays with an accuracy of 98.7%, which offer an opportunity to further explore these methods for prospective use.
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Santosh KC, Ghosh S, GhoshRoy D. Deep Learning for Covid-19 Screening Using Chest X-Rays in 2020: A Systematic Review. INT J PATTERN RECOGN 2022. [DOI: 10.1142/s0218001422520103] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Artificial Intelligence (AI) has promoted countless contributions in the field of healthcare and medical imaging. In this paper, we thoroughly analyze peer-reviewed research findings/articles on AI-guided tools for Covid-19 analysis/screening using chest X-ray images in the year 2020. We discuss on how far deep learning algorithms help in decision-making. We identify/address data collections, methodical contributions, promising methods, and challenges. However, a fair comparison is not trivial as dataset sizes vary over time, throughout the year 2020. Even though their unprecedented efforts in building AI-guided tools to detect, localize, and segment Covid-19 cases are limited to education and training, we elaborate on their strengths and possible weaknesses when we consider the need of cross-population train/test models. In total, with search keywords: (Covid-19 OR Coronavirus) AND chest x-ray AND deep learning AND artificial intelligence AND medical imaging in both PubMed Central Repository and Web of Science, we systematically reviewed 58 research articles and performed meta-analysis.
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Affiliation(s)
- KC Santosh
- 2AI: Applied Artificial Intelligence Research Lab – Computer Science, University of South Dakota, Vermillion, SD 57069, USA
| | - Supriti Ghosh
- 2AI: Applied Artificial Intelligence Research Lab – Computer Science, University of South Dakota, Vermillion, SD 57069, USA
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Pan L, Ji B, Wang H, Wang L, Liu M, Chongcheawchamnan M, Peng S. MFDNN: multi-channel feature deep neural network algorithm to identify COVID19 chest X-ray images. Health Inf Sci Syst 2022; 10:4. [PMID: 35432950 PMCID: PMC9004212 DOI: 10.1007/s13755-022-00174-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Accepted: 04/04/2022] [Indexed: 11/23/2022] Open
Abstract
The use of chest X-ray images (CXI) to detect Severe Acute Respiratory Syndrome Coronavirus 2 (SARS CoV-2) caused by Coronavirus Disease 2019 (COVID19) is life-saving important for both patients and doctors. This research proposes a multi-channel feature deep neural network (MFDNN) algorithm to screen people infected with COVID19. The algorithm integrates data over-sampling technology and MFDNN model to carry out the training. The oversampling technique reduces the deviation of the prior probability of the MFDNN algorithm on unbalanced data. Multi-channel feature fusion technology improves the efficiency of feature extraction and the accuracy of model diagnosis. In the experiment, Compared with traditional deep learning models (VGG19, GoogLeNet, Resnet50, Desnet201), the MFDNN model obtains an average test accuracy of 93.19% in all data. Furthermore, in each type of screening, the precision, recall, and F1 Score of the MFDNN model are also better than traditional deep learning networks. Furthermore, through ablation experiments, we proved that a multi-channel convolutional neural network (CNN) is superior to single-channel CNN, additional layer and PSN module, and indirectly proved the sufficiency and necessity of each step of the MFDNN classification method. Finally, our experimental code will be placed at https://github.com/panliangrui/covid19.
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Muhammad U, Hoque MZ, Oussalah M, Keskinarkaus A, Seppänen T, Sarder P. SAM: Self-augmentation mechanism for COVID-19 detection using chest X-ray images. Knowl Based Syst 2022; 241:108207. [PMID: 35068707 PMCID: PMC8762871 DOI: 10.1016/j.knosys.2022.108207] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 01/07/2022] [Accepted: 01/08/2022] [Indexed: 12/20/2022]
Abstract
COVID-19 is a rapidly spreading viral disease and has affected over 100 countries worldwide. The numbers of casualties and cases of infection have escalated particularly in countries with weakened healthcare systems. Recently, reverse transcription-polymerase chain reaction (RT-PCR) is the test of choice for diagnosing COVID-19. However, current evidence suggests that COVID-19 infected patients are mostly stimulated from a lung infection after coming in contact with this virus. Therefore, chest X-ray (i.e., radiography) and chest CT can be a surrogate in some countries where PCR is not readily available. This has forced the scientific community to detect COVID-19 infection from X-ray images and recently proposed machine learning methods offer great promise for fast and accurate detection. Deep learning with convolutional neural networks (CNNs) has been successfully applied to radiological imaging for improving the accuracy of diagnosis. However, the performance remains limited due to the lack of representative X-ray images available in public benchmark datasets. To alleviate this issue, we propose a self-augmentation mechanism for data augmentation in the feature space rather than in the data space using reconstruction independent component analysis (RICA). Specifically, a unified architecture is proposed which contains a deep convolutional neural network (CNN), a feature augmentation mechanism, and a bidirectional LSTM (BiLSTM). The CNN provides the high-level features extracted at the pooling layer where the augmentation mechanism chooses the most relevant features and generates low-dimensional augmented features. Finally, BiLSTM is used to classify the processed sequential information. We conducted experiments on three publicly available databases to show that the proposed approach achieves the state-of-the-art results with accuracy of 97%, 84% and 98%. Explainability analysis has been carried out using feature visualization through PCA projection and t-SNE plots.
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Affiliation(s)
- Usman Muhammad
- Center for Machine Vision and Signal Analysis, Faculty of Information Technology and Electrical Engineering, University of Oulu, Finland
| | - Md Ziaul Hoque
- Center for Machine Vision and Signal Analysis, Faculty of Information Technology and Electrical Engineering, University of Oulu, Finland
| | - Mourad Oussalah
- Center for Machine Vision and Signal Analysis, Faculty of Information Technology and Electrical Engineering, University of Oulu, Finland.,Medical Imaging, Physics, and Technology (MIPT), Faculty of Medicine, University of Oulu, Finland
| | - Anja Keskinarkaus
- Center for Machine Vision and Signal Analysis, Faculty of Information Technology and Electrical Engineering, University of Oulu, Finland
| | - Tapio Seppänen
- Center for Machine Vision and Signal Analysis, Faculty of Information Technology and Electrical Engineering, University of Oulu, Finland
| | - Pinaki Sarder
- Department of Pathology and Anatomical Sciences, University at Buffalo, USA
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Ogunjo ST, Fuwape IA, Rabiu AB. Predicting COVID-19 Cases From Atmospheric Parameters Using Machine Learning Approach. GEOHEALTH 2022; 6:e2021GH000509. [PMID: 35415381 PMCID: PMC8983058 DOI: 10.1029/2021gh000509] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Revised: 02/06/2022] [Accepted: 02/28/2022] [Indexed: 06/14/2023]
Abstract
The dynamical nature of COVID-19 cases in different parts of the world requires robust mathematical approaches for prediction and forecasting. In this study, we aim to (a) forecast future COVID-19 cases based on past infections, (b) predict current COVID-19 cases using PM2.5, temperature, and humidity data, using four different machine learning classifiers (Decision Tree, K-nearest neighbor, Support Vector Machine, and Random Forest). Based on RMSE values, k-nearest neighbor and support vector machine algorithms were found to be the best for predicting future incidences of COVID-19 based on past histories. From the RMSE values obtained, temperature was found to be the best predictor for number of COVID-19 cases, followed by relative humidity. Decision tree models was found to perform poorly in the prediction of COVID-19 cases considering particulate matter and atmospheric parameters as predictors. Our results suggests the possibility of predicting virus infection using machine learning. This will guide policy makers in proactive monitoring and control.
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Affiliation(s)
- S. T. Ogunjo
- Department of PhysicsFederal University of Technology AkureAkureNigeria
| | - I. A. Fuwape
- Department of PhysicsFederal University of Technology AkureAkureNigeria
- Office of the Vice ChancellorMichael and Cecilia Ibru UniversityUghelliNigeria
| | - A. B. Rabiu
- Centre for Atmospheric ResearchNational Space and Research Development AgencyAnyigbaNigeria
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Agarwal D, Randall P, White Z, Bisnette B, Dickson J, Allen C, Chamani F, Prakash P, Ade C, Natarajan B. A Non-Invasive Hydration Monitoring Technique Using Microwave Transmission and Data-Driven Approaches. SENSORS (BASEL, SWITZERLAND) 2022; 22:2536. [PMID: 35408154 PMCID: PMC9003514 DOI: 10.3390/s22072536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 03/20/2022] [Accepted: 03/20/2022] [Indexed: 06/14/2023]
Abstract
Dehydration in the human body arises due to inadequate replenishment of fluids. An appropriate level of hydration is essential for optimal functioning of the human body, and complications ranging from mild discomfort to, in severe cases, death, could result from a neglected imbalance in fluid levels. Regular and accurate monitoring of hydration status can provide meaningful information for people operating in stressful environmental conditions, such as athletes, military professionals and the elderly. In this study, we propose a non-invasive hydration monitoring technique employing non-ionizing electromagnetic power in the microwave band to estimate the changes in the water content of the whole body. Specifically, we investigate changes in the attenuation coefficient in the frequency range 2-3.5 GHz between a pair of planar antennas positioned across a participant's arm during various states of hydration. Twenty healthy young adults (10M, 10F) underwent controlled hypohydration and euhydration control bouts. The attenuation coefficient was compared among trials and used to predict changes in body mass. Volunteers lost 1.50±0.44% and 0.49±0.54% body mass during hypohydration and euhydration, respectively. The microwave transmission-based attenuation coefficient (2-3.5 GHz) was accurate in predicting changes in hydration status. The corresponding regression analysis demonstrates that building separate estimation models for dehydration and rehydration phases offer better predictive performance (88%) relative to a common model for both the phases (76%).
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Affiliation(s)
- Deepesh Agarwal
- Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS 66506, USA; (D.A.); (P.R.); (B.B.); (J.D.); (C.A.); (F.C.); (B.N.)
| | - Philip Randall
- Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS 66506, USA; (D.A.); (P.R.); (B.B.); (J.D.); (C.A.); (F.C.); (B.N.)
| | - Zachary White
- Department of Kinesiology, Kansas State University, Manhattan, KS 66506, USA; (Z.W.); (C.A.)
| | - Bayleigh Bisnette
- Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS 66506, USA; (D.A.); (P.R.); (B.B.); (J.D.); (C.A.); (F.C.); (B.N.)
| | - Jenalee Dickson
- Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS 66506, USA; (D.A.); (P.R.); (B.B.); (J.D.); (C.A.); (F.C.); (B.N.)
| | - Cross Allen
- Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS 66506, USA; (D.A.); (P.R.); (B.B.); (J.D.); (C.A.); (F.C.); (B.N.)
| | - Faraz Chamani
- Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS 66506, USA; (D.A.); (P.R.); (B.B.); (J.D.); (C.A.); (F.C.); (B.N.)
| | - Punit Prakash
- Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS 66506, USA; (D.A.); (P.R.); (B.B.); (J.D.); (C.A.); (F.C.); (B.N.)
| | - Carl Ade
- Department of Kinesiology, Kansas State University, Manhattan, KS 66506, USA; (Z.W.); (C.A.)
| | - Balasubramaniam Natarajan
- Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS 66506, USA; (D.A.); (P.R.); (B.B.); (J.D.); (C.A.); (F.C.); (B.N.)
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Alyasseri ZAA, Al‐Betar MA, Doush IA, Awadallah MA, Abasi AK, Makhadmeh SN, Alomari OA, Abdulkareem KH, Adam A, Damasevicius R, Mohammed MA, Zitar RA. Review on COVID-19 diagnosis models based on machine learning and deep learning approaches. EXPERT SYSTEMS 2022; 39:e12759. [PMID: 34511689 PMCID: PMC8420483 DOI: 10.1111/exsy.12759] [Citation(s) in RCA: 58] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 05/17/2021] [Accepted: 06/07/2021] [Indexed: 05/02/2023]
Abstract
COVID-19 is the disease evoked by a new breed of coronavirus called the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Recently, COVID-19 has become a pandemic by infecting more than 152 million people in over 216 countries and territories. The exponential increase in the number of infections has rendered traditional diagnosis techniques inefficient. Therefore, many researchers have developed several intelligent techniques, such as deep learning (DL) and machine learning (ML), which can assist the healthcare sector in providing quick and precise COVID-19 diagnosis. Therefore, this paper provides a comprehensive review of the most recent DL and ML techniques for COVID-19 diagnosis. The studies are published from December 2019 until April 2021. In general, this paper includes more than 200 studies that have been carefully selected from several publishers, such as IEEE, Springer and Elsevier. We classify the research tracks into two categories: DL and ML and present COVID-19 public datasets established and extracted from different countries. The measures used to evaluate diagnosis methods are comparatively analysed and proper discussion is provided. In conclusion, for COVID-19 diagnosing and outbreak prediction, SVM is the most widely used machine learning mechanism, and CNN is the most widely used deep learning mechanism. Accuracy, sensitivity, and specificity are the most widely used measurements in previous studies. Finally, this review paper will guide the research community on the upcoming development of machine learning for COVID-19 and inspire their works for future development. This review paper will guide the research community on the upcoming development of ML and DL for COVID-19 and inspire their works for future development.
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Affiliation(s)
- Zaid Abdi Alkareem Alyasseri
- Center for Artificial Intelligence Technology, Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangiMalaysia
- ECE Department‐Faculty of EngineeringUniversity of KufaNajafIraq
| | - Mohammed Azmi Al‐Betar
- Artificial Intelligence Research Center (AIRC)Ajman UniversityAjmanUnited Arab Emirates
- Department of Information TechnologyAl‐Huson University College, Al‐Balqa Applied UniversityIrbidJordan
| | - Iyad Abu Doush
- Computing Department, College of Engineering and Applied SciencesAmerican University of KuwaitSalmiyaKuwait
- Computer Science DepartmentYarmouk UniversityIrbidJordan
| | - Mohammed A. Awadallah
- Artificial Intelligence Research Center (AIRC)Ajman UniversityAjmanUnited Arab Emirates
- Department of Computer ScienceAl‐Aqsa UniversityGazaPalestine
| | - Ammar Kamal Abasi
- Artificial Intelligence Research Center (AIRC)Ajman UniversityAjmanUnited Arab Emirates
- School of Computer SciencesUniversiti Sains MalaysiaPenangMalaysia
| | - Sharif Naser Makhadmeh
- Artificial Intelligence Research Center (AIRC)Ajman UniversityAjmanUnited Arab Emirates
- Faculty of Information TechnologyMiddle East UniversityAmmanJordan
| | | | | | - Afzan Adam
- Center for Artificial Intelligence Technology, Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangiMalaysia
| | | | - Mazin Abed Mohammed
- College of Computer Science and Information TechnologyUniversity of AnbarAnbarIraq
| | - Raed Abu Zitar
- Sorbonne Center of Artificial IntelligenceSorbonne University‐Abu DhabiAbu DhabiUnited Arab Emirates
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50
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Wei B, Li C, Xu J. The Relationship between Intelligent Image Simulation and Recognition Technology and the Health Literacy and Quality of Life of the Elderly. CONTRAST MEDIA & MOLECULAR IMAGING 2022; 2022:9984873. [PMID: 35280704 PMCID: PMC8890847 DOI: 10.1155/2022/9984873] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 12/15/2021] [Accepted: 01/25/2022] [Indexed: 12/29/2022]
Abstract
In order to explore the relationship between intelligent image recognition technology and the mentality and quality of life of the elderly, this paper combines intelligent image simulation technology to identify the behavior of the elderly, protect the safety of the elderly, and provide timely feedback on the adverse conditions of the elderly. Moreover, this paper improves the traditional intelligent image recognition algorithm, verifies the research method of this paper through experimental research, and puts forward corresponding suggestions. Through investigation and research, we can see that the level of health literacy of elderly patients with chronic diseases is low. Therefore, in the future health education, we should strengthen health education for elderly patients with chronic diseases, use different mass media to propagate health knowledge, and promote the formation of healthy lifestyles and behaviors for elderly patients with chronic diseases. At the same time, the experiment also verified that the intelligent image recognition technology proposed in this paper has a positive effect in improving the mentality and quality of life of the elderly.
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Affiliation(s)
- Baojian Wei
- Yanbian University, Yanji, China
- Shandong First Medical University and Shandong Academy of Medical Sciences, Taian, China
| | | | - Jiangye Xu
- School of Mechanical Electrical and Information Engineering, China University of Mining and Technology Beijing Campus, Beijing 100083, China
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