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Dabbagh R, Jamal A, Bhuiyan Masud JH, Titi MA, Amer YS, Khayat A, Alhazmi TS, Hneiny L, Baothman FA, Alkubeyyer M, Khan SA, Temsah MH. Harnessing Machine Learning in Early COVID-19 Detection and Prognosis: A Comprehensive Systematic Review. Cureus 2023; 15:e38373. [PMID: 37265897 PMCID: PMC10230599 DOI: 10.7759/cureus.38373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/30/2023] [Indexed: 06/03/2023] Open
Abstract
During the early phase of the COVID-19 pandemic, reverse transcriptase-polymerase chain reaction (RT-PCR) testing faced limitations, prompting the exploration of machine learning (ML) alternatives for diagnosis and prognosis. Providing a comprehensive appraisal of such decision support systems and their use in COVID-19 management can aid the medical community in making informed decisions during the risk assessment of their patients, especially in low-resource settings. Therefore, the objective of this study was to systematically review the studies that predicted the diagnosis of COVID-19 or the severity of the disease using ML. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA), we conducted a literature search of MEDLINE (OVID), Scopus, EMBASE, and IEEE Xplore from January 1 to June 31, 2020. The outcomes were COVID-19 diagnosis or prognostic measures such as death, need for mechanical ventilation, admission, and acute respiratory distress syndrome. We included peer-reviewed observational studies, clinical trials, research letters, case series, and reports. We extracted data about the study's country, setting, sample size, data source, dataset, diagnostic or prognostic outcomes, prediction measures, type of ML model, and measures of diagnostic accuracy. Bias was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). This study was registered in the International Prospective Register of Systematic Reviews (PROSPERO), with the number CRD42020197109. The final records included for data extraction were 66. Forty-three (64%) studies used secondary data. The majority of studies were from Chinese authors (30%). Most of the literature (79%) relied on chest imaging for prediction, while the remainder used various laboratory indicators, including hematological, biochemical, and immunological markers. Thirteen studies explored predicting COVID-19 severity, while the rest predicted diagnosis. Seventy percent of the articles used deep learning models, while 30% used traditional ML algorithms. Most studies reported high sensitivity, specificity, and accuracy for the ML models (exceeding 90%). The overall concern about the risk of bias was "unclear" in 56% of the studies. This was mainly due to concerns about selection bias. ML may help identify COVID-19 patients in the early phase of the pandemic, particularly in the context of chest imaging. Although these studies reflect that these ML models exhibit high accuracy, the novelty of these models and the biases in dataset selection make using them as a replacement for the clinicians' cognitive decision-making questionable. Continued research is needed to enhance the robustness and reliability of ML systems in COVID-19 diagnosis and prognosis.
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Affiliation(s)
- Rufaidah Dabbagh
- Family & Community Medicine Department, College of Medicine, King Saud University, Riyadh, SAU
| | - Amr Jamal
- Family & Community Medicine Department, College of Medicine, King Saud University, Riyadh, SAU
- Research Chair for Evidence-Based Health Care and Knowledge Translation, Family and Community Medicine Department, College of Medicine, King Saud University, Riyadh, SAU
| | | | - Maher A Titi
- Quality Management Department, King Saud University Medical City, Riyadh, SAU
- Research Chair for Evidence-Based Health Care and Knowledge Translation, Family and Community Medicine Department, College of Medicine, King Saud University, Riyadh, SAU
| | - Yasser S Amer
- Pediatrics, Quality Management Department, King Saud University Medical City, Riyadh, SAU
- Research Chair for Evidence-Based Health Care and Knowledge Translation, Family and Community Medicine Department, College of Medicine, King Saud University, Riyadh, SAU
| | - Afnan Khayat
- Health Information Management Department, Prince Sultan Military College of Health Sciences, Al Dhahran, SAU
| | - Taha S Alhazmi
- Family & Community Medicine Department, College of Medicine, King Saud University, Riyadh, SAU
| | - Layal Hneiny
- Medicine, Wegner Health Sciences Library, University of South Dakota, Vermillion, USA
| | - Fatmah A Baothman
- Department of Information Systems, King Abdulaziz University, Jeddah, SAU
| | | | - Samina A Khan
- School of Computer Sciences, Universiti Sains Malaysia, Penang, MYS
| | - Mohamad-Hani Temsah
- Pediatric Intensive Care Unit, Department of Pediatrics, King Saud University, Riyadh, SAU
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Soundrapandiyan R, Naidu H, Karuppiah M, Maheswari M, Poonia RC. AI-based wavelet and stacked deep learning architecture for detecting coronavirus (COVID-19) from chest X-ray images. COMPUTERS & ELECTRICAL ENGINEERING : AN INTERNATIONAL JOURNAL 2023; 108:108711. [PMID: 37065503 PMCID: PMC10086108 DOI: 10.1016/j.compeleceng.2023.108711] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 03/29/2023] [Accepted: 03/31/2023] [Indexed: 06/19/2023]
Abstract
A novel coronavirus (COVID-19), belonging to a family of severe acute respiratory syndrome coronavirus 2 (SARs-CoV-2), was identified in Wuhan city, Hubei, China, in November 2019. The disease had already infected more than 681.529665 million people as of March 13, 2023. Hence, early detection and diagnosis of COVID-19 are essential. For this purpose, radiologists use medical images such as X-ray and computed tomography (CT) images for the diagnosis of COVID-19. It is very difficult for researchers to help radiologists to do automatic diagnoses by using traditional image processing methods. Therefore, a novel artificial intelligence (AI)-based deep learning model to detect COVID-19 from chest X-ray images is proposed. The proposed work uses a wavelet and stacked deep learning architecture (ResNet50, VGG19, Xception, and DarkNet19) named WavStaCovNet-19 to detect COVID-19 from chest X-ray images automatically. The proposed work has been tested on two publicly available datasets and achieved an accuracy of 94.24% and 96.10% on 4 classes and 3 classes, respectively. From the experimental results, we believe that the proposed work can surely be useful in the healthcare domain to detect COVID-19 with less time and cost, and with higher accuracy.
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Affiliation(s)
- Rajkumar Soundrapandiyan
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India
| | | | - Marimuthu Karuppiah
- School of Computer Science and Engineering & Information Science, Presidency University, Bengaluru, Karnataka 560064, India
| | - M Maheswari
- Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai 600119, India
| | - Ramesh Chandra Poonia
- Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, Karnataka 560029, India
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53
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Karbasi Z, Gohari SH, Sabahi A. Bibliometric analysis of the use of artificial intelligence in COVID-19 based on scientific studies. Health Sci Rep 2023; 6:e1244. [PMID: 37152228 PMCID: PMC10158785 DOI: 10.1002/hsr2.1244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 04/11/2023] [Accepted: 04/16/2023] [Indexed: 05/09/2023] Open
Abstract
Background and Aims One such strategy is citation analysis used by researchers for research planning an article referred to by another article receives a "citation." By using bibliometric analysis, the development of research areas and authors' influence can be investigated. The current study aimed to identify and analyze the characteristics of 100 highly cited articles on the use of artificial intelligence concerning COVID-19. Methods On July 27, 2022, this database was searched using the keywords "artificial intelligence" and "COVID-19" in the topic. After extensive searching, all retrieved articles were sorted by the number of citations, and 100 highly cited articles were included based on the number of citations. The following data were extracted: year of publication, type of study, name of journal, country, number of citations, language, and keywords. Results The average number of citations for 100 highly cited articles was 138.54. The top three cited articles with 745, 596, and 549 citations. The top 100 articles were all in English and were published in 2020 and 2021. China was the most prolific country with 19 articles, followed by the United States with 15 articles and India with 10 articles. Conclusion The current bibliometric analysis demonstrated the significant growth of the use of artificial intelligence for COVID-19. Using these results, research priorities are more clearly defined, and researchers can focus on hot topics.
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Affiliation(s)
- Zahra Karbasi
- Medical Informatics Research Center, Institute for Futures Studies in HealthKerman University of Medical SciencesKermanIran
- Department of Health Information Sciences, Faculty of Management and Medical Information SciencesKerman University of Medical SciencesKermanIran
| | - Sadrieh H. Gohari
- Medical Informatics Research Center, Institute for Futures Studies in HealthKerman University of Medical SciencesKermanIran
| | - Azam Sabahi
- Department of Health Information Technology, Ferdows School of Health and Allied Medical SciencesBirjand University of Medical SciencesBirjandIran
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Borhani A, Afyouni S, Attari MMA, Mohseni A, Catalano O, Kamel IR. PET/MR enterography in inflammatory bowel disease: A review of applications and technical considerations. Eur J Radiol 2023; 163:110846. [PMID: 37121100 DOI: 10.1016/j.ejrad.2023.110846] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 04/18/2023] [Accepted: 04/20/2023] [Indexed: 05/02/2023]
Abstract
Positron emission tomography (PET) magnetic resonance (MR) enterography is a novel hybrid imaging technique that is gaining popularity in the study of complex inflammatory disorders of the gastrointestinal system, such as inflammatory bowel disease (IBD). This imaging technique combines the metabolic information of PET imaging with the spatial resolution and soft tissue contrast of MR imaging. Several studies have suggested potential roles for PET/MR imaging in determining the activity status of IBD, evaluating treatment response, stratifying risk, and predicting long-term clinical outcomes. However, there are challenges in generalizing findings due to limited studies, technical aspects of hybrid MR/PET imaging, and clinical indications of this imaging modality. This review aims to further elucidate the possible role of PET/MR in IBD, highlight important technical aspects of imaging, and address potential pitfalls and prospects of this modality in IBDs.
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Affiliation(s)
- Ali Borhani
- Russell H. Morgan Department of Radiology and Radiological Sciences, John's Hopkins Medicine, John's Hopkins University, Baltimore, MD, United States
| | - Shadi Afyouni
- Russell H. Morgan Department of Radiology and Radiological Sciences, John's Hopkins Medicine, John's Hopkins University, Baltimore, MD, United States
| | - Mohammad Mirza Aghazadeh Attari
- Russell H. Morgan Department of Radiology and Radiological Sciences, John's Hopkins Medicine, John's Hopkins University, Baltimore, MD, United States
| | - Alireza Mohseni
- Russell H. Morgan Department of Radiology and Radiological Sciences, John's Hopkins Medicine, John's Hopkins University, Baltimore, MD, United States
| | - Onofrio Catalano
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, United States; Harvard Medical School, 25 Shattuck St, Boston, MA 02115, United States
| | - Ihab R Kamel
- Russell H. Morgan Department of Radiology and Radiological Sciences, John's Hopkins Medicine, John's Hopkins University, Baltimore, MD, United States.
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Velu M, Dhanaraj RK, Balusamy B, Kadry S, Yu Y, Nadeem A, Rauf HT. Human Pathogenic Monkeypox Disease Recognition Using Q-Learning Approach. Diagnostics (Basel) 2023; 13:diagnostics13081491. [PMID: 37189591 DOI: 10.3390/diagnostics13081491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 03/15/2023] [Accepted: 04/12/2023] [Indexed: 05/17/2023] Open
Abstract
While the world is working quietly to repair the damage caused by COVID-19's widespread transmission, the monkeypox virus threatens to become a global pandemic. There are several nations that report new monkeypox cases daily, despite the virus being less deadly and contagious than COVID-19. Monkeypox disease may be detected using artificial intelligence techniques. This paper suggests two strategies for improving monkeypox image classification precision. Based on reinforcement learning and parameter optimization for multi-layer neural networks, the suggested approaches are based on feature extraction and classification: the Q-learning algorithm determines the rate at which an act occurs in a particular state; Malneural networks are binary hybrid algorithms that improve the parameters of neural networks. The algorithms are evaluated using an openly available dataset. In order to analyze the proposed optimization feature selection for monkeypox classification, interpretation criteria were utilized. In order to evaluate the efficiency, significance, and robustness of the suggested algorithms, a series of numerical tests were conducted. There were 95% precision, 95% recall, and 96% f1 scores for monkeypox disease. As compared to traditional learning methods, this method has a higher accuracy value. The overall macro average was around 0.95, and the overall weighted average was around 0.96. When compared to the benchmark algorithms, DDQN, Policy Gradient, and Actor-Critic, the Malneural network had the highest accuracy (around 0.985). In comparison with traditional methods, the proposed methods were found to be more effective. Clinicians can use this proposal to treat monkeypox patients and administration agencies can use it to observe the origin and current status of the disease.
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Affiliation(s)
- Malathi Velu
- School of Computer Science and Engineering, Panimalar Engineering College, Poonamallee, Chennai 600123, India
| | - Rajesh Kumar Dhanaraj
- School of Computing Science and Engineering, Galgotias University, Greater Noida 203201, India
| | - Balamurugan Balusamy
- Associate Dean-Student Engagement, Shiv Nadar Institution of Eminence, Delhi-National Capital Region (NCR), Gautam Buddha Nagar 201314, India
| | - Seifedine Kadry
- Department of Applied Data Science, Noroff University College, 4612 Kristiansand, Norway
- Artificial Intelligence Research Center (AIRC), Ajman University, Ajman P.O. Box 346, United Arab Emirates
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos 13-5053, Lebanon
| | - Yang Yu
- Centre for Infrastructure Engineering and Safety (CIES), The University of New South Wales, Sydney, NSW 2052, Australia
| | - Ahmed Nadeem
- Department of Pharmacology and Toxicology, College of Pharmacy, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia
| | - Hafiz Tayyab Rauf
- Centre for Smart Systems, A.I. and Cybersecurity, Staffordshire University, Stoke-on-Trent ST4 2DE, UK
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Chen CC, Huang JF, Lin WC, Cheng CT, Chen SC, Fu CY, Lee MS, Liao CH, Chung CY. The Feasibility and Performance of Total Hip Replacement Prediction Deep Learning Algorithm with Real World Data. Bioengineering (Basel) 2023; 10:bioengineering10040458. [PMID: 37106645 PMCID: PMC10136253 DOI: 10.3390/bioengineering10040458] [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: 02/07/2023] [Revised: 03/15/2023] [Accepted: 04/04/2023] [Indexed: 04/29/2023] Open
Abstract
(1) Background: Hip degenerative disorder is a common geriatric disease is the main causes to lead to total hip replacement (THR). The surgical timing of THR is crucial for post-operative recovery. Deep learning (DL) algorithms can be used to detect anomalies in medical images and predict the need for THR. The real world data (RWD) were used to validate the artificial intelligence and DL algorithm in medicine but there was no previous study to prove its function in THR prediction. (2) Methods: We designed a sequential two-stage hip replacement prediction deep learning algorithm to identify the possibility of THR in three months of hip joints by plain pelvic radiography (PXR). We also collected RWD to validate the performance of this algorithm. (3) Results: The RWD totally included 3766 PXRs from 2018 to 2019. The overall accuracy of the algorithm was 0.9633; sensitivity was 0.9450; specificity was 1.000 and the precision was 1.000. The negative predictive value was 0.9009, the false negative rate was 0.0550, and the F1 score was 0.9717. The area under curve was 0.972 with 95% confidence interval from 0.953 to 0.987. (4) Conclusions: In summary, this DL algorithm can provide an accurate and reliable method for detecting hip degeneration and predicting the need for further THR. RWD offered an alternative support of the algorithm and validated its function to save time and cost.
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Affiliation(s)
- Chih-Chi Chen
- Department of Physical Medicine and Rehabilitation, Chang Gung Memorial Hospital, Chang Gung University, Linkou, Taoyuan 33328, Taiwan
| | - Jen-Fu Huang
- Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Chang Gung University, Linkou, Taoyuan 33328, Taiwan
| | - Wei-Cheng Lin
- Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Chang Gung University, Linkou, Taoyuan 33328, Taiwan
- Department of Electrical Engineering, Chang Gung University, Taoyuan 33302, Taiwan
| | - Chi-Tung Cheng
- Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Chang Gung University, Linkou, Taoyuan 33328, Taiwan
| | - Shann-Ching Chen
- Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Chang Gung University, Linkou, Taoyuan 33328, Taiwan
| | - Chih-Yuan Fu
- Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Chang Gung University, Linkou, Taoyuan 33328, Taiwan
| | - Mel S Lee
- Department of Orthopaedic Surgery, Pao-Chien Hospital, Pingtung 90078, Taiwan
| | - Chien-Hung Liao
- Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Chang Gung University, Linkou, Taoyuan 33328, Taiwan
| | - Chia-Ying Chung
- Department of Physical Medicine and Rehabilitation, Chang Gung Memorial Hospital, Chang Gung University, Linkou, Taoyuan 33328, Taiwan
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Khattab R, Abdelmaksoud IR, Abdelrazek S. Deep Convolutional Neural Networks for Detecting COVID-19 Using Medical Images: A Survey. NEW GENERATION COMPUTING 2023; 41:343-400. [PMID: 37229176 PMCID: PMC10071474 DOI: 10.1007/s00354-023-00213-6] [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: 06/14/2022] [Accepted: 02/23/2023] [Indexed: 05/27/2023]
Abstract
Coronavirus Disease 2019 (COVID-19), which is caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2), surprised the world in December 2019 and has threatened the lives of millions of people. Countries all over the world closed worship places and shops, prevented gatherings, and implemented curfews to stand against the spread of COVID-19. Deep Learning (DL) and Artificial Intelligence (AI) can have a great role in detecting and fighting this disease. Deep learning can be used to detect COVID-19 symptoms and signs from different imaging modalities, such as X-Ray, Computed Tomography (CT), and Ultrasound Images (US). This could help in identifying COVID-19 cases as a first step to curing them. In this paper, we reviewed the research studies conducted from January 2020 to September 2022 about deep learning models that were used in COVID-19 detection. This paper clarified the three most common imaging modalities (X-Ray, CT, and US) in addition to the DL approaches that are used in this detection and compared these approaches. This paper also provided the future directions of this field to fight COVID-19 disease.
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Affiliation(s)
- Rana Khattab
- Information Systems Department, Faculty of Computers and Information, Mansoura University, Mansoura, Egypt
| | - Islam R. Abdelmaksoud
- Information Systems Department, Faculty of Computers and Information, Mansoura University, Mansoura, Egypt
| | - Samir Abdelrazek
- Information Systems Department, Faculty of Computers and Information, Mansoura University, Mansoura, Egypt
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Gürsoy E, Kaya Y. An overview of deep learning techniques for COVID-19 detection: methods, challenges, and future works. MULTIMEDIA SYSTEMS 2023; 29:1603-1627. [PMID: 37261262 PMCID: PMC10039775 DOI: 10.1007/s00530-023-01083-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Accepted: 03/20/2023] [Indexed: 06/02/2023]
Abstract
The World Health Organization (WHO) declared a pandemic in response to the coronavirus COVID-19 in 2020, which resulted in numerous deaths worldwide. Although the disease appears to have lost its impact, millions of people have been affected by this virus, and new infections still occur. Identifying COVID-19 requires a reverse transcription-polymerase chain reaction test (RT-PCR) or analysis of medical data. Due to the high cost and time required to scan and analyze medical data, researchers are focusing on using automated computer-aided methods. This review examines the applications of deep learning (DL) and machine learning (ML) in detecting COVID-19 using medical data such as CT scans, X-rays, cough sounds, MRIs, ultrasound, and clinical markers. First, the data preprocessing, the features used, and the current COVID-19 detection methods are divided into two subsections, and the studies are discussed. Second, the reported publicly available datasets, their characteristics, and the potential comparison materials mentioned in the literature are presented. Third, a comprehensive comparison is made by contrasting the similar and different aspects of the studies. Finally, the results, gaps, and limitations are summarized to stimulate the improvement of COVID-19 detection methods, and the study concludes by listing some future research directions for COVID-19 classification.
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Affiliation(s)
- Ercan Gürsoy
- Department of Computer Engineering, Adana Alparslan Turkes Science and Technology University, 01250 Adana, Turkey
| | - Yasin Kaya
- Department of Computer Engineering, Adana Alparslan Turkes Science and Technology University, 01250 Adana, Turkey
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Detection and classification of COVID-19 by using faster R-CNN and mask R-CNN on CT images. Neural Comput Appl 2023; 35:13597-13611. [PMCID: PMC10014413 DOI: 10.1007/s00521-023-08450-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 02/28/2023] [Indexed: 03/17/2023]
Abstract
The coronavirus (COVID-19) pandemic has a devastating impact on people’s daily lives and healthcare systems. The rapid spread of this virus should be stopped by early detection of infected patients through efficient screening. Artificial intelligence techniques are used for accurate disease detection in computed tomography (CT) images. This article aims to develop a process that can accurately diagnose COVID-19 using deep learning techniques on CT images. Using CT images collected from Yozgat Bozok University, the presented method begins with the creation of an original dataset, which includes 4000 CT images. The faster R-CNN and mask R-CNN methods are presented for this purpose in order to train and test the dataset to categorize patients with COVID-19 and pneumonia infections. In this study, the results are compared using VGG-16 for faster R-CNN model and ResNet-50 and ResNet-101 backbones for mask R-CNN. The faster R-CNN model used in the study has an accuracy rate of 93.86%, and the ROI (region of interest) classification loss is 0.061 per ROI. At the conclusion of the final training, the mask R-CNN model generates mAP (mean average precision) values for ResNet-50 and ResNet-101, respectively, of 97.72% and 95.65%. The results for five folds are obtained by applying the cross-validation to the methods used. With training, our model performs better than the industry standard baselines and can help with automated COVID-19 severity quantification in CT images.
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Bhosale YH, Patnaik KS. Bio-medical imaging (X-ray, CT, ultrasound, ECG), genome sequences applications of deep neural network and machine learning in diagnosis, detection, classification, and segmentation of COVID-19: a Meta-analysis & systematic review. MULTIMEDIA TOOLS AND APPLICATIONS 2023:1-54. [PMID: 37362676 PMCID: PMC10015538 DOI: 10.1007/s11042-023-15029-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 02/01/2023] [Accepted: 02/27/2023] [Indexed: 06/28/2023]
Abstract
This review investigates how Deep Machine Learning (DML) has dealt with the Covid-19 epidemic and provides recommendations for future Covid-19 research. Despite the fact that vaccines for this epidemic have been developed, DL methods have proven to be a valuable asset in radiologists' arsenals for the automated assessment of Covid-19. This detailed review debates the techniques and applications developed for Covid-19 findings using DL systems. It also provides insights into notable datasets used to train neural networks, data partitioning, and various performance measurement metrics. The PRISMA taxonomy has been formed based on pretrained(45 systems) and hybrid/custom(17 systems) models with radiography modalities. A total of 62 systems with respect to X-ray(32), CT(19), ultrasound(7), ECG(2), and genome sequence(2) based modalities as taxonomy are selected from the studied articles. We originate by valuing the present phase of DL and conclude with significant limitations. The restrictions contain incomprehensibility, simplification measures, learning from incomplete labeled data, and data secrecy. Moreover, DML can be utilized to detect and classify Covid-19 from other COPD illnesses. The proposed literature review has found many DL-based systems to fight against Covid19. We expect this article will assist in speeding up the procedure of DL for Covid-19 researchers, including medical, radiology technicians, and data engineers.
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Affiliation(s)
- Yogesh H. Bhosale
- Computer Science and Engineering Department, Birla Institute of Technology, Mesra, Ranchi, India
| | - K. Sridhar Patnaik
- Computer Science and Engineering Department, Birla Institute of Technology, Mesra, Ranchi, India
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Shaheed K, Szczuko P, Abbas Q, Hussain A, Albathan M. Computer-Aided Diagnosis of COVID-19 from Chest X-ray Images Using Hybrid-Features and Random Forest Classifier. Healthcare (Basel) 2023; 11:healthcare11060837. [PMID: 36981494 PMCID: PMC10047954 DOI: 10.3390/healthcare11060837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 03/09/2023] [Accepted: 03/10/2023] [Indexed: 03/16/2023] Open
Abstract
In recent years, a lot of attention has been paid to using radiology imaging to automatically find COVID-19. (1) Background: There are now a number of computer-aided diagnostic schemes that help radiologists and doctors perform diagnostic COVID-19 tests quickly, accurately, and consistently. (2) Methods: Using chest X-ray images, this study proposed a cutting-edge scheme for the automatic recognition of COVID-19 and pneumonia. First, a pre-processing method based on a Gaussian filter and logarithmic operator is applied to input chest X-ray (CXR) images to improve the poor-quality images by enhancing the contrast, reducing the noise, and smoothing the image. Second, robust features are extracted from each enhanced chest X-ray image using a Convolutional Neural Network (CNNs) transformer and an optimal collection of grey-level co-occurrence matrices (GLCM) that contain features such as contrast, correlation, entropy, and energy. Finally, based on extracted features from input images, a random forest machine learning classifier is used to classify images into three classes, such as COVID-19, pneumonia, or normal. The predicted output from the model is combined with Gradient-weighted Class Activation Mapping (Grad-CAM) visualisation for diagnosis. (3) Results: Our work is evaluated using public datasets with three different train–test splits (70–30%, 80–20%, and 90–10%) and achieved an average accuracy, F1 score, recall, and precision of 97%, 96%, 96%, and 96%, respectively. A comparative study shows that our proposed method outperforms existing and similar work. The proposed approach can be utilised to screen COVID-19-infected patients effectively. (4) Conclusions: A comparative study with the existing methods is also performed. For performance evaluation, metrics such as accuracy, sensitivity, and F1-measure are calculated. The performance of the proposed method is better than that of the existing methodologies, and it can thus be used for the effective diagnosis of the disease.
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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
| | - Piotr Szczuko
- 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
| | - Mubarak Albathan
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia;
- Correspondence: ; Tel.: +966-503451575
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Yadav DP, Jalal AS, Goyal A, Mishra A, Uprety K, Guragai N. COVID-19 radiograph prognosis using a deep CResNeXt network. MULTIMEDIA TOOLS AND APPLICATIONS 2023; 82:1-27. [PMID: 37362635 PMCID: PMC9993361 DOI: 10.1007/s11042-023-14960-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 04/07/2022] [Accepted: 02/22/2023] [Indexed: 06/28/2023]
Abstract
COVID-19 has caused an epidemic in the entire world and it is caused by the novel virus SARS-COV-2. In severe conditions, this virus can cause a critical lung infection or viral pneumonia. To administer the correct treatment to patients, COVID-19 testing is important for diagnosing and determining patients who are infected with COVID-19, as opposed to those infected with other bacterial or viral infections. In this paper, a CResNeXt chest radiograph COVID-19 prediction model is proposed using residual network architecture. The advantage of the proposed model is that it requires lesser free hyper-parameters as compared to other residual networks. In addition, the training time per epochs of the model is very less compared to VGG19, ResNet-50, ResNeXt. The proposed CResNeXt model's binary classification (COVID-19 versus No-Finding) accuracy is observed to be 98.63% and 99.99% and multi-class classification (COVID-19, Pneumonia, and No-Finding) accuracy is observed to be 97.42% and 99.27% on the original and augmented datasets, respectively.
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Affiliation(s)
- Dhirendra P. Yadav
- Department of Computer Engineering & Applications, G.L.A. University, Mathura, UP India
| | - Anand Singh Jalal
- Department of Computer Engineering & Applications, G.L.A. University, Mathura, UP India
| | - Ayush Goyal
- Department of Electrical Engineering and Computer Science, Texas A&M University, Kingsville, TX USA
| | - Avdesh Mishra
- Department of Electrical Engineering and Computer Science, Texas A&M University, Kingsville, TX USA
| | - Khem Uprety
- The University of Tennessee Health Science Center, Memphis, TN USA
| | - Nirmal Guragai
- Department of Cardiology, St. Joseph Regional Medical Center, Paterson, NJ USA
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Moschovos C, Tsivgoulis G, Ghika A, Bakola E, Papadopoulou M, Zis P, Zouvelou V, Salakou S, Papagiannopoulou G, Kotsali-Peteinelli V, Chroni E, Kyrozis A. Image analysis can reliably quantify median nerve echogenicity and texture changes in patients with carpal tunnel syndrome. Clin Neurophysiol 2023; 149:61-69. [PMID: 36907099 DOI: 10.1016/j.clinph.2023.02.171] [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: 06/28/2022] [Revised: 01/24/2023] [Accepted: 02/22/2023] [Indexed: 03/07/2023]
Abstract
OBJECTIVE To study the ability of image analysis measures to quantify echotexture changes of median nerve in order to provide a complementary diagnostic tool in CTS. METHODS Image analysis measures (gray level co-occurrence matrix (GLCM), brightness, hypoechoic area percentage using max entropy and mean threshold) were calculated in normalized images of 39 (19 younger and 20 older than 65y) healthy controls and 95 CTS patients (37 younger and 58 older than 65y). RESULTS Image analysis measures were equivalent or superior (older patients) to subjective visual analysis. In younger patients, GLCM measures showed equivalent diagnostic accuracy with cross sectional area (CSA) (Area Under Curve (AUC for inverse different moment = 0.97). In older patients all image analysis measures showed similar diagnostic accuracy to CSA (AUC for brightness = 0.88). Moreover, they had abnormal values in many older patients with normal CSA values. CONCLUSIONS Image analysis reliably quantifies median nerve echotexture alterations in CTS and offers similar diagnostic accuracy to CSA measurement. SIGNIFICANCE Image analysis may offer added value to existing measures in the evaluation of CTS, especially in older patients. Its clinical implementation would require incorporation of mathematically simple software code for online nerve image analysis in ultrasound machines.
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Affiliation(s)
- Christos Moschovos
- Second Department of Neurology, "Attikon" Hospital, School of Medicine, National and Kapodistrian University of Athens, Chaidari 12462, Athens, Greece.
| | - Georgios Tsivgoulis
- Second Department of Neurology, "Attikon" Hospital, School of Medicine, National and Kapodistrian University of Athens, Chaidari 12462, Athens, Greece; First Department of Neurology, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Apostolia Ghika
- First Department of Neurology, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece
| | - Eleni Bakola
- Second Department of Neurology, "Attikon" Hospital, School of Medicine, National and Kapodistrian University of Athens, Chaidari 12462, Athens, Greece
| | - Marianna Papadopoulou
- Second Department of Neurology, "Attikon" Hospital, School of Medicine, National and Kapodistrian University of Athens, Chaidari 12462, Athens, Greece
| | - Panagiotis Zis
- Second Department of Neurology, "Attikon" Hospital, School of Medicine, National and Kapodistrian University of Athens, Chaidari 12462, Athens, Greece
| | - Vasiliki Zouvelou
- First Department of Neurology, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece
| | - Stavroula Salakou
- First Department of Neurology, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Georgia Papagiannopoulou
- Second Department of Neurology, "Attikon" Hospital, School of Medicine, National and Kapodistrian University of Athens, Chaidari 12462, Athens, Greece
| | - Vassiliki Kotsali-Peteinelli
- Second Department of Neurology, "Attikon" Hospital, School of Medicine, National and Kapodistrian University of Athens, Chaidari 12462, Athens, Greece
| | | | - Andreas Kyrozis
- First Department of Neurology, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece
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Shukla AK, Seth T, Muhuri PK. Artificial intelligence centric scientific research on COVID-19: an analysis based on scientometrics data. MULTIMEDIA TOOLS AND APPLICATIONS 2023; 82:1-33. [PMID: 37362722 PMCID: PMC9978294 DOI: 10.1007/s11042-023-14642-4] [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: 02/07/2022] [Revised: 07/01/2022] [Accepted: 02/03/2023] [Indexed: 06/28/2023]
Abstract
With the spread of the deadly coronavirus disease throughout the geographies of the globe, expertise from every field has been sought to fight the impact of the virus. The use of Artificial Intelligence (AI), especially, has been the center of attention due to its capability to produce trustworthy results in a reasonable time. As a result, AI centric based research on coronavirus (or COVID-19) has been receiving growing attention from different domains ranging from medicine, virology, and psychiatry etc. We present this comprehensive study that closely monitors the impact of the pandemic on global research activities related exclusively to AI. In this article, we produce highly informative insights pertaining to publications, such as the best articles, research areas, most productive and influential journals, authors, and institutions. Studies are made on top 50 most cited articles to identify the most influential AI subcategories. We also study the outcome of research from different geographic areas while identifying the research collaborations that have had an impact. This study also compares the outcome of research from the different countries around the globe and produces insights on the same.
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Affiliation(s)
- Amit K. Shukla
- Faculty of Information Technology, University of Jyväskylä, Box 35 (Agora), Jyväskylä, 40014 Finland
| | - Taniya Seth
- Department of Computer Science, South Asian University, Akbar Bhawan, Chanakyapuri, New Delhi 110021 India
| | - Pranab K. Muhuri
- Department of Computer Science, South Asian University, Akbar Bhawan, Chanakyapuri, New Delhi 110021 India
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65
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Keerthana R, Gladston A, Nehemiah HK. Transfer learning-based CNN diagnostic framework for diagnosis of COVID-19 from lung CT images. THE IMAGING SCIENCE JOURNAL 2023. [DOI: 10.1080/13682199.2023.2170768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
Affiliation(s)
- R. Keerthana
- Department of Computer Science and Engineering, Anna University Chennai, Chennai, India
| | - Angelin Gladston
- Department of Computer Science and Engineering, Anna University Chennai, Chennai, India
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66
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Akpan EB, Nunez SE, Sibbitt WL. Ultrasound-based measures in carpal tunnel syndrome: What to measure? JOURNAL OF CLINICAL ULTRASOUND : JCU 2023; 51:507-509. [PMID: 36893034 DOI: 10.1002/jcu.23386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 10/22/2022] [Accepted: 10/25/2022] [Indexed: 06/18/2023]
Abstract
Carpal tunnel syndrome (CTS) is the most common peripheral entrapment neuropathy and is caused by compression of the median nerve (MN) at the level of transverse carpal ligament of the volar wrist. Radiomics is an advanced semi-automated image analysis method that is utilized to identify characteristics in the MN that can detect CTS with considerable reproducibility.
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Affiliation(s)
- Eyerusalem B Akpan
- Department of Internal Medicine, Division of Rheumatology, University of New Mexico Health Science Center, Albuquerque, New Mexico, USA
| | - Sharon E Nunez
- Department of Internal Medicine, Division of Rheumatology, University of New Mexico Health Science Center, Albuquerque, New Mexico, USA
| | - Wilmer L Sibbitt
- Department of Internal Medicine, Division of Rheumatology, University of New Mexico Health Science Center, Albuquerque, New Mexico, USA
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Lyu S, Zhang Y, Zhang M, Jiang M, Yu J, Zhu J, Zhang B. Ultrasound-based radiomics in the diagnosis of carpal tunnel syndrome: The influence of regions of interest delineation method on mode. JOURNAL OF CLINICAL ULTRASOUND : JCU 2023; 51:498-506. [PMID: 36341718 DOI: 10.1002/jcu.23387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 10/05/2022] [Accepted: 10/07/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND In the recent years, artificial intelligence (AI) algorithms have been used to accurately diagnose musculoskeletal diseases. However, it is not known whether the particular regions of interest (ROI) delineation method would affect the performance of the AI algorithm. PURPOSE The purpose of this study was to investigate the influence of ROI delineation methods on model performance and observer consistency. METHODS In this retrospective analysis, ultrasound (US) measures of median nerves affected with carpal tunnel syndrome (CTS) were compared to median nerves in a control group without CTS. Two methods were used for delineation of the ROI: (1) the ROI along the hyperechoic medial edge of the median nerve but not including the epineurium (MN) (ROI1); and (2) the ROI including the hyperechoic epineurium (ROI2), respectively. The intra group correlation coefficient (ICC) was used to compare the observer consistency of ROI features (i.e. the corresponding radiomics parameters). Parameters α1 and α2 were obtained based on the ICC of ROI1 features and ROI2 features. The ROC analysis was used to determine the area under the curve (AUC) and evaluate the performance of the radiologists and network. In addition, four indices, namely sensitivity, specificity, positive prediction and negative prediction were analyzed too. RESULTS A total of 136 wrists of 77 CTS group and 136 wrists of 74 control group were included in the study. Control group was matched to CTS group according to the age and sex. The observer consistency of ROI features delineated by the two schemes was different, and the consistency of ROI1 features was higher (α1 ˃ α2). The intra-observer consistency was higher than the inter-observer consistency regardless of the scheme, and the intra-observer consistency was higher when chose scheme one. The performances of models based on the two ROI features were different, although the AUC of each model was greater than 0.8.The model performed better when the MN epineurium was included in the ROI. Among five artificial intelligence algorithms, the Forest models (model1 achieved an AUC of 0.921 in training datasets and 0.830 in testing datasets; model2 achieved an AUC of 0.967 in training datasets and 0.872 in testing datasets.) obtained the highest performance, followed by the support vector machine (SVM) models and the Logistic models. The performances of the models were significantly better than the inexperienced radiologist (Dr. B. Z. achieved an AUC of 0.702). CONCLUSION Different ROI delineation methods may affect the performance of the model and the consistency of observers. Model performance was better when the ROI contained the MN epineurium, and observer consistency was higher when the ROI was delineated along the hyperechoic medial border of the MN.
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Affiliation(s)
- Shuyi Lyu
- Department of Interventional Therapy, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, People's Republic of China
- Ningbo Clinical Research Center for Medical Imaging, Ningbo, China
- Provinicial and Municipal Co-construction Key Discipline for Medical Imaging, Ningbo, China
| | - Yan Zhang
- Department of Interventional Therapy, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, People's Republic of China
- Ningbo Clinical Research Center for Medical Imaging, Ningbo, China
- Provinicial and Municipal Co-construction Key Discipline for Medical Imaging, Ningbo, China
| | - Meiwu Zhang
- Department of Interventional Therapy, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, People's Republic of China
- Ningbo Clinical Research Center for Medical Imaging, Ningbo, China
- Provinicial and Municipal Co-construction Key Discipline for Medical Imaging, Ningbo, China
| | - Maoqing Jiang
- Ningbo Clinical Research Center for Medical Imaging, Ningbo, China
- Provinicial and Municipal Co-construction Key Discipline for Medical Imaging, Ningbo, China
- Department of PET-CT and Nuclear Medicine, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, People's Republic of China
| | - Jianjun Yu
- Department of Neuroelectrophysiology, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, People's Republic of China
| | - Jiazhen Zhu
- Ningbo Clinical Research Center for Medical Imaging, Ningbo, China
- Provinicial and Municipal Co-construction Key Discipline for Medical Imaging, Ningbo, China
- Multi-disciplinary diagnosis and treatment department, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, People's Republic of China
| | - Baisong Zhang
- Department of Interventional Therapy, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, People's Republic of China
- Ningbo Clinical Research Center for Medical Imaging, Ningbo, China
- Provinicial and Municipal Co-construction Key Discipline for Medical Imaging, Ningbo, China
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Kurt Z, Işık Ş, Kaya Z, Anagün Y, Koca N, Çiçek S. Evaluation of EfficientNet models for COVID-19 detection using lung parenchyma. Neural Comput Appl 2023; 35:12121-12132. [PMID: 36843903 PMCID: PMC9940669 DOI: 10.1007/s00521-023-08344-z] [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: 06/22/2022] [Accepted: 01/25/2023] [Indexed: 02/23/2023]
Abstract
When the COVID-19 pandemic broke out in the beginning of 2020, it became crucial to enhance early diagnosis with efficient means to reduce dangers and future spread of the viruses as soon as possible. Finding effective treatments and lowering mortality rates is now more important than ever. Scanning with a computer tomography (CT) scanner is a helpful method for detecting COVID-19 in this regard. The present paper, as such, is an attempt to contribute to this process by generating an open-source, CT-based image dataset. This dataset contains the CT scans of lung parenchyma regions of 180 COVID-19-positive and 86 COVID-19-negative patients taken at the Bursa Yuksek Ihtisas Training and Research Hospital. The experimental studies show that the modified EfficientNet-ap-nish method uses this dataset effectively for diagnostic purposes. Firstly, a smart segmentation mechanism based on the k-means algorithm is applied to this dataset as a preprocessing stage. Then, performance pretrained models are analyzed using different CNN architectures and with our Nish activation function. The statistical rates are obtained by the various EfficientNet models and the highest detection score is obtained with the EfficientNet-B4-ap-nish version, which provides a 97.93% accuracy rate and a 97.33% F1-score. The implications of the proposed method are immense both for present-day applications and future developments.
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Affiliation(s)
- Zuhal Kurt
- Department of Computer Engineering, Atilim University, Ankara, Turkey
| | - Şahin Işık
- Department of Computer Engineering, Eskisehir Osmangazi University Meselik Campus, Eskisehir, Turkey
| | - Zeynep Kaya
- Department of Electrical and Energy, Bilecik Seyh Edebali University, Osmaneli Vocational School, Bilecik, Turkey
| | - Yıldıray Anagün
- Department of Computer Engineering, Eskisehir Osmangazi University Meselik Campus, Eskisehir, Turkey
| | - Nizameddin Koca
- Department of Internal Medicine, University of Health Sciences, Bursa Yuksek Ihtisas Training and Research Hospital, Bursa, Turkey
| | - Sümeyye Çiçek
- Department of Internal Medicine, University of Health Sciences, Bursa Yuksek Ihtisas Training and Research Hospital, Bursa, Turkey
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A Novel Proposal for Deep Learning-Based Diabetes Prediction: Converting Clinical Data to Image Data. Diagnostics (Basel) 2023; 13:diagnostics13040796. [PMID: 36832284 PMCID: PMC9955314 DOI: 10.3390/diagnostics13040796] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 02/14/2023] [Accepted: 02/15/2023] [Indexed: 02/22/2023] Open
Abstract
Diabetes, one of the most common diseases worldwide, has become an increasingly global threat to humans in recent years. However, early detection of diabetes greatly inhibits the progression of the disease. This study proposes a new method based on deep learning for the early detection of diabetes. Like many other medical data, the PIMA dataset used in the study contains only numerical values. In this sense, the application of popular convolutional neural network (CNN) models to such data are limited. This study converts numerical data into images based on the feature importance to use the robust representation of CNN models in early diabetes diagnosis. Three different classification strategies are then applied to the resulting diabetes image data. In the first, diabetes images are fed into the ResNet18 and ResNet50 CNN models. In the second, deep features of the ResNet models are fused and classified with support vector machines (SVM). In the last approach, the selected fusion features are classified by SVM. The results demonstrate the robustness of diabetes images in the early diagnosis of diabetes.
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70
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Aslani S, Jacob J. Utilisation of deep learning for COVID-19 diagnosis. Clin Radiol 2023; 78:150-157. [PMID: 36639173 PMCID: PMC9831845 DOI: 10.1016/j.crad.2022.11.006] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 11/21/2022] [Accepted: 11/22/2022] [Indexed: 01/12/2023]
Abstract
The COVID-19 pandemic that began in 2019 has resulted in millions of deaths worldwide. Over this period, the economic and healthcare consequences of COVID-19 infection in survivors of acute COVID-19 infection have become apparent. During the course of the pandemic, computer analysis of medical images and data have been widely used by the medical research community. In particular, deep-learning methods, which are artificial intelligence (AI)-based approaches, have been frequently employed. This paper provides a review of deep-learning-based AI techniques for COVID-19 diagnosis using chest radiography and computed tomography. Thirty papers published from February 2020 to March 2022 that used two-dimensional (2D)/three-dimensional (3D) deep convolutional neural networks combined with transfer learning for COVID-19 detection were reviewed. The review describes how deep-learning methods detect COVID-19, and several limitations of the proposed methods are highlighted.
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Affiliation(s)
- S Aslani
- Centre for Medical Image Computing and Department of Respiratory Medicine, University College London, London, UK.
| | - J Jacob
- Centre for Medical Image Computing and Department of Respiratory Medicine, University College London, London, UK
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Yıldırım G, Karakaş HM, Özkaya YA, Şener E, Fındık Ö, Pulat GN. Development of an Artificial Intelligence Method to Detect COVID-19 Pneumonia in Computed Tomography Images. ISTANBUL MEDICAL JOURNAL 2023. [DOI: 10.4274/imj.galenos.2023.07348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/09/2023] Open
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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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Chen Y, Lin Y, Xu X, Ding J, Li C, Zeng Y, Xie W, Huang J. Multi-domain medical image translation generation for lung image classification based on generative adversarial networks. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 229:107200. [PMID: 36525713 DOI: 10.1016/j.cmpb.2022.107200] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 10/20/2022] [Accepted: 10/21/2022] [Indexed: 06/17/2023]
Abstract
OBJECTIVE Lung image classification-assisted diagnosis has a large application market. Aiming at the problems of poor attention to existing translation models, the insufficient ability of key transfer and generation, insufficient quality of generated images, and lack of detailed features, this paper conducts research on lung medical image translation and lung image classification based on generative adversarial networks. METHODS This paper proposes a medical image multi-domain translation algorithm MI-GAN based on the key migration branch. After the actual analysis of the imbalanced medical image data, the key target domain images are selected, the key migration branch is established, and a single generator is used to complete the medical image multi-domain translation. The conversion between domains ensures the attention performance of the medical image multi-domain translation model and the quality of the synthesized images. At the same time, a lung image classification model based on synthetic image data augmentation is proposed. The synthetic lung CT medical images and the original real medical images are used as the training set together to study the performance of the auxiliary diagnosis model in the classification of normal healthy subjects, and also of the mild and severe COVID-19 patients. RESULTS Based on the chest CT image dataset, MI-GAN has completed the mutual conversion and generation of normal lung images without disease, viral pneumonia and Mild COVID-19 images. The synthetic images GAN-test and GAN-train indicators reached, respectively 92.188% and 85.069%, compared with other generative models in terms of authenticity and diversity, there is a considerable improvement. The accuracy rate of pneumonia diagnosis of the lung image classification model is 93.85%, which is 3.1% higher than that of the diagnosis model trained only with real images; the sensitivity of disease diagnosis is 96.69%, a relative improvement of 7.1%. 1%, the specificity was 89.70%; the area under the ROC curve (AUC) increased from 94.00% to 96.17%. CONCLUSION In this paper, a multi-domain translation model of medical images based on the key transfer branch is proposed, which enables the translation network to have key transfer and attention performance. It is verified on lung CT images and achieved good results. The required medical images are synthesized by the above medical image translation model, and the effectiveness of the synthesized images on the lung image classification network is verified experimentally.
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Affiliation(s)
- Yunfeng Chen
- Department of Pulmonary Medicine, The Second Affiliated Hospital of Fujian Medical University, 950 Eastsea street, Fengzhe District, Quanzhou, Fujian 362000, China.
| | - Yalan Lin
- Department of Pulmonary Medicine, The Second Affiliated Hospital of Fujian Medical University, 950 Eastsea street, Fengzhe District, Quanzhou, Fujian 362000, China
| | - Xiaodie Xu
- Department of Pulmonary Medicine, The Second Affiliated Hospital of Fujian Medical University, 950 Eastsea street, Fengzhe District, Quanzhou, Fujian 362000, China
| | - Jinzhen Ding
- Department of Pulmonary Medicine, The Second Affiliated Hospital of Fujian Medical University, 950 Eastsea street, Fengzhe District, Quanzhou, Fujian 362000, China
| | - Chuzhao Li
- Department of Pulmonary Medicine, The Second Affiliated Hospital of Fujian Medical University, 950 Eastsea street, Fengzhe District, Quanzhou, Fujian 362000, China
| | - Yiming Zeng
- Department of Pulmonary Medicine, The Second Affiliated Hospital of Fujian Medical University, 950 Eastsea street, Fengzhe District, Quanzhou, Fujian 362000, China.
| | - Weifang Xie
- Faculty of Mathematics and Computer Science, Quanzhou Normal University, Quanzhou 362000, China; Fujian Provincial Key Laboratory of Data Intensive Computing, Quanzhou 362000, China; Key Laboratory of Intelligent Computing and Information Processing, Fujian Province University, Quanzhou 362000, China
| | - Jianlong Huang
- Faculty of Mathematics and Computer Science, Quanzhou Normal University, Quanzhou 362000, China; Fujian Provincial Key Laboratory of Data Intensive Computing, Quanzhou 362000, China; Key Laboratory of Intelligent Computing and Information Processing, Fujian Province University, Quanzhou 362000, China
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Elseddik M, Mostafa RR, Elashry A, El-Rashidy N, El-Sappagh S, Elgamal S, Aboelfetouh A, El-Bakry H. Predicting CTS Diagnosis and Prognosis Based on Machine Learning Techniques. Diagnostics (Basel) 2023; 13:diagnostics13030492. [PMID: 36766597 PMCID: PMC9914125 DOI: 10.3390/diagnostics13030492] [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: 12/26/2022] [Revised: 01/12/2023] [Accepted: 01/20/2023] [Indexed: 01/31/2023] Open
Abstract
Carpal tunnel syndrome (CTS) is a clinical disease that occurs due to compression of the median nerve in the carpal tunnel. The determination of the severity of carpal tunnel syndrome is essential to provide appropriate therapeutic interventions. Machine learning (ML)-based modeling can be used to classify diseases, make decisions, and create new therapeutic interventions. It is also used in medical research to implement predictive models. However, despite the growth in medical research based on ML and Deep Learning (DL), CTS research is still relatively scarce. While a few studies have developed models to predict diagnosis of CTS, no ML model has been presented to classify the severity of CTS based on comprehensive clinical data. Therefore, this study developed new classification models for determining CTS severity using ML algorithms. This study included 80 patients with other diseases that have an overlap in symptoms with CTS, such as cervical radiculopathysasas, de quervian tendinopathy, and peripheral neuropathy, and 80 CTS patients who underwent ultrasonography (US)-guided median nerve hydrodissection. CTS severity was classified into mild, moderate, and severe grades. In our study, we aggregated the data from CTS patients and patients with other diseases that have an overlap in symptoms with CTS, such as cervical radiculopathysasas, de quervian tendinopathy, and peripheral neuropathy. The dataset was randomly split into training and test data, at 70% and 30%, respectively. The proposed model achieved promising results of 0.955%, 0.963%, and 0.919% in terms of classification accuracy, precision, and recall, respectively. In addition, we developed a machine learning model that predicts the probability of a patient improving after the hydro-dissection injection process based on the aggregated data after three different months (one, three, and six). The proposed model achieved accuracy after six months of 0.912%, after three months of 0.901%, and after one month 0.877%. The overall performance for predicting the prognosis after six months outperforms the prediction after one and three months. We utilized statistics tests (significance test, Spearman's correlation test, and two-way ANOVA test) to determine the effect of injection process in CTS treatment. Our data-driven decision support tools can be used to help determine which patients to operate on in order to avoid the associated risks and expenses of surgery.
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Affiliation(s)
- Marwa Elseddik
- Department of the Robotics and Internet Machines, Faculty of Artificial Intelligence, Kafrelsheikh University, Kafr El Sheikh 33516, Egypt
- Department of Information Systems, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt
| | - Reham R. Mostafa
- Department of Information Systems, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt
| | - Ahmed Elashry
- Department of Information Systems, Faculty of Computers and Information, Kafrelsheiksh University, Kafr El Sheikh 33516, Egypt
| | - Nora El-Rashidy
- Department of Machine Learning and Information Retrieval, Faculty of Artificial Intelligence, Kafrelsheiksh University, Kafr El Sheikh 33516, Egypt
- Correspondence: (N.E.-R.); (S.E.-S.)
| | - Shaker El-Sappagh
- Faculty of Computer Science and Engineering, Galala University, Suez 43511, Egypt
- Information Systems Department, Faculty of Computers and Artificial Intelligence, Benha University, Banha 13518, Egypt
- Correspondence: (N.E.-R.); (S.E.-S.)
| | - Shimaa Elgamal
- Department of Neuropsychiatry, Faculty of Medicine, Kafrelsheiksh University, Kafr El Sheikh 33516, Egypt
| | - Ahmed Aboelfetouh
- Department of Information Systems, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt
- Delta Higher Institute for Management and Accounting Information Systems, Mansoura 35511, Egypt
| | - Hazem El-Bakry
- Department of Information Systems, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt
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Lightweight ResGRU: a deep learning-based prediction of SARS-CoV-2 (COVID-19) and its severity classification using multimodal chest radiography images. Neural Comput Appl 2023; 35:9637-9655. [PMID: 36714075 PMCID: PMC9873217 DOI: 10.1007/s00521-023-08200-0] [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: 01/28/2022] [Accepted: 01/06/2023] [Indexed: 01/26/2023]
Abstract
The new COVID-19 emerged in a town in China named Wuhan in December 2019, and since then, this deadly virus has infected 324 million people worldwide and caused 5.53 million deaths by January 2022. Because of the rapid spread of this pandemic, different countries are facing the problem of a shortage of resources, such as medical test kits and ventilators, as the number of cases increased uncontrollably. Therefore, developing a readily available, low-priced, and automated approach for COVID-19 identification is the need of the hour. The proposed study uses chest radiography images (CRIs) such as X-rays and computed tomography (CTs) to detect chest infections, as these modalities contain important information about chest infections. This research introduces a novel hybrid deep learning model named Lightweight ResGRU that uses residual blocks and a bidirectional gated recurrent unit to diagnose non-COVID and COVID-19 infections using pre-processed CRIs. Lightweight ResGRU is used for multi-modal two-class classification (normal and COVID-19), three-class classification (normal, COVID-19, and viral pneumonia), four-class classification (normal, COVID-19, viral pneumonia, and bacterial pneumonia), and COVID-19 severity types' classification (i.e., atypical appearance, indeterminate appearance, typical appearance, and negative for pneumonia). The proposed architecture achieved f-measure of 99.0%, 98.4%, 91.0%, and 80.5% for two-class, three-class, four-class, and COVID-19 severity level classifications, respectively, on unseen data. A large dataset is created by combining and changing different publicly available datasets. The results prove that radiologists can adopt this method to screen chest infections where test kits are limited.
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76
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Vinod DN, Prabaharan SRS. COVID-19-The Role of Artificial Intelligence, Machine Learning, and Deep Learning: A Newfangled. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2023; 30:2667-2682. [PMID: 36685135 PMCID: PMC9843670 DOI: 10.1007/s11831-023-09882-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 01/05/2023] [Indexed: 05/29/2023]
Abstract
The absolute previously infected novel coronavirus (COVID-19) was found in Wuhan, China, in December 2019. The COVID-19 epidemic has spread to more than 220 nations and territories globally and has altogether influenced each part of our day-to-day lives. As of 9th March 2022, a total aggregate of 44,78,82,185 (60,07,317) contaminated (dead) COVID-19 cases were accounted for all over the world. The quantities of contaminated cases passing despite everything increment essentially and do not indicate a controlled circumstance. The scope of this paper is to address this issue by presenting a comprehensive and comparative analysis of the existing Machine Learning (ML), Deep Learning (DL) and Artificial Intelligence (AI) based approaches used in significance in reacting to the COVID-19 epidemic and diagnosing the severe impacts. The paper provides, firstly, an overview of COVID-19 infection and highlights of this article; Secondly, an overview of exploring various executive innovations by utilizing different resources to stop the spread of COVID-19; Thirdly, a comparison of existing predicting methods of COVID-19 in the literature, with focus on ML, DL and AI-driven techniques with performance metrics; and finally, a discussion on the results of the work as well as future scope.
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Affiliation(s)
- Dasari Naga Vinod
- Department of Electronics and Communication Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, Tamil Nadu 600062 India
| | - S. R. S. Prabaharan
- Sathyabama Centre for Advanced Studies, Sathyabama Institute of Science and Technology, Rajiv Gandhi Salai, Chennai, Tamil Nadu 600119 India
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77
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Alhares H, Tanha J, Balafar MA. AMTLDC: a new adversarial multi-source transfer learning framework to diagnosis of COVID-19. EVOLVING SYSTEMS 2023; 14:1-15. [PMID: 38625255 PMCID: PMC9838404 DOI: 10.1007/s12530-023-09484-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 01/02/2023] [Indexed: 01/13/2023]
Abstract
In recent years, deep learning techniques have been widely used to diagnose diseases. However, in some tasks, such as the diagnosis of COVID-19 disease, due to insufficient data, the model is not properly trained and as a result, the generalizability of the model decreases. For example, if the model is trained on a CT scan dataset and tested on another CT scan dataset, it predicts near-random results. To address this, data from several different sources can be combined using transfer learning, taking into account the intrinsic and natural differences in existing datasets obtained with different medical imaging tools and approaches. In this paper, to improve the transfer learning technique and better generalizability between multiple data sources, we propose a multi-source adversarial transfer learning model, namely AMTLDC. In AMTLDC, representations are learned that are similar among the sources. In other words, extracted representations are general and not dependent on the particular dataset domain. We apply the AMTLDC to predict Covid-19 from medical images using a convolutional neural network. We show that accuracy can be improved using the AMTLDC framework, and surpass the results of current successful transfer learning approaches. In particular, we show that the AMTLDC works well when using different dataset domains, or when there is insufficient data.
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Affiliation(s)
- Hadi Alhares
- Department of Computer Engineering, Faculty of Electrical and Computer Engineering, University of Tabriz, 29th Bahman Blvd, Tabriz, 5166616471 Iran
| | - Jafar Tanha
- Department of Computer Engineering, Faculty of Electrical and Computer Engineering, University of Tabriz, 29th Bahman Blvd, Tabriz, 5166616471 Iran
| | - Mohammad Ali Balafar
- Department of Computer Engineering, Faculty of Electrical and Computer Engineering, University of Tabriz, 29th Bahman Blvd, Tabriz, 5166616471 Iran
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78
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Mercaldo F, Belfiore MP, Reginelli A, Brunese L, Santone A. Coronavirus covid-19 detection by means of explainable deep learning. Sci Rep 2023; 13:462. [PMID: 36627339 PMCID: PMC9830129 DOI: 10.1038/s41598-023-27697-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 01/05/2023] [Indexed: 01/12/2023] Open
Abstract
The coronavirus is caused by the infection of the SARS-CoV-2 virus: it represents a complex and new condition, considering that until the end of December 2019 this virus was totally unknown to the international scientific community. The clinical management of patients with the coronavirus disease has undergone an evolution over the months, thanks to the increasing knowledge of the virus, symptoms and efficacy of the various therapies. Currently, however, there is no specific therapy for SARS-CoV-2 virus, know also as Coronavirus disease 19, and treatment is based on the symptoms of the patient taking into account the overall clinical picture. Furthermore, the test to identify whether a patient is affected by the virus is generally performed on sputum and the result is generally available within a few hours or days. Researches previously found that the biomedical imaging analysis is able to show signs of pneumonia. For this reason in this paper, with the aim of providing a fully automatic and faster diagnosis, we design and implement a method adopting deep learning for the novel coronavirus disease detection, starting from computed tomography medical images. The proposed approach is aimed to detect whether a computed tomography medical images is related to an healthy patient, to a patient with a pulmonary disease or to a patient affected with Coronavirus disease 19. In case the patient is marked by the proposed method as affected by the Coronavirus disease 19, the areas symptomatic of the Coronavirus disease 19 infection are automatically highlighted in the computed tomography medical images. We perform an experimental analysis to empirically demonstrate the effectiveness of the proposed approach, by considering medical images belonging from different institutions, with an average time for Coronavirus disease 19 detection of approximately 8.9 s and an accuracy equal to 0.95.
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Affiliation(s)
- Francesco Mercaldo
- Department of Medicine and Health Sciences "Vincenzo Tiberio", University of Molise, Campobasso, Italy.
| | - Maria Paola Belfiore
- Department of Precision Medicine, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Alfonso Reginelli
- Department of Precision Medicine, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Luca Brunese
- Department of Medicine and Health Sciences "Vincenzo Tiberio", University of Molise, Campobasso, Italy
| | - Antonella Santone
- Department of Medicine and Health Sciences "Vincenzo Tiberio", University of Molise, Campobasso, Italy
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79
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Podder P, Das SR, Mondal MRH, Bharati S, Maliha A, Hasan MJ, Piltan F. LDDNet: A Deep Learning Framework for the Diagnosis of Infectious Lung Diseases. SENSORS (BASEL, SWITZERLAND) 2023; 23:480. [PMID: 36617076 PMCID: PMC9824583 DOI: 10.3390/s23010480] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 12/25/2022] [Accepted: 12/27/2022] [Indexed: 06/17/2023]
Abstract
This paper proposes a new deep learning (DL) framework for the analysis of lung diseases, including COVID-19 and pneumonia, from chest CT scans and X-ray (CXR) images. This framework is termed optimized DenseNet201 for lung diseases (LDDNet). The proposed LDDNet was developed using additional layers of 2D global average pooling, dense and dropout layers, and batch normalization to the base DenseNet201 model. There are 1024 Relu-activated dense layers and 256 dense layers using the sigmoid activation method. The hyper-parameters of the model, including the learning rate, batch size, epochs, and dropout rate, were tuned for the model. Next, three datasets of lung diseases were formed from separate open-access sources. One was a CT scan dataset containing 1043 images. Two X-ray datasets comprising images of COVID-19-affected lungs, pneumonia-affected lungs, and healthy lungs exist, with one being an imbalanced dataset with 5935 images and the other being a balanced dataset with 5002 images. The performance of each model was analyzed using the Adam, Nadam, and SGD optimizers. The best results have been obtained for both the CT scan and CXR datasets using the Nadam optimizer. For the CT scan images, LDDNet showed a COVID-19-positive classification accuracy of 99.36%, a 100% precision recall of 98%, and an F1 score of 99%. For the X-ray dataset of 5935 images, LDDNet provides a 99.55% accuracy, 73% recall, 100% precision, and 85% F1 score using the Nadam optimizer in detecting COVID-19-affected patients. For the balanced X-ray dataset, LDDNet provides a 97.07% classification accuracy. For a given set of parameters, the performance results of LDDNet are better than the existing algorithms of ResNet152V2 and XceptionNet.
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Affiliation(s)
- Prajoy Podder
- Institute of Information and Communication Technology, Bangladesh University of Engineering and Technology, Dhaka 1205, Bangladesh
| | - Sanchita Rani Das
- Institute of Information and Communication Technology, Bangladesh University of Engineering and Technology, Dhaka 1205, Bangladesh
| | - M. Rubaiyat Hossain Mondal
- Institute of Information and Communication Technology, Bangladesh University of Engineering and Technology, Dhaka 1205, Bangladesh
| | - Subrato Bharati
- Institute of Information and Communication Technology, Bangladesh University of Engineering and Technology, Dhaka 1205, Bangladesh
| | - Azra Maliha
- Faculty of Engineering and IT, The British University in Dubai, Dubai P.O. Box 345015, United Arab Emirates
| | - Md Junayed Hasan
- National Subsea Centre, Robert Gordon University, Aberdeen AB10 7AQ, UK
| | - Farzin Piltan
- Ulsan Industrial Artificial Intelligence (UIAI) Lab, Department of Electrical, Electronics and Computer Engineering, University of Ulsan, Ulsan 44610, Republic of Korea
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80
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Lin C, Huang Y, Wang W, Feng S, Feng S. Lesion detection of chest X-Ray based on scalable attention residual CNN. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:1730-1749. [PMID: 36899506 DOI: 10.3934/mbe.2023079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Most of the research on disease recognition in chest X-rays is limited to segmentation and classification, but the problem of inaccurate recognition in edges and small parts makes doctors spend more time making judgments. In this paper, we propose a lesion detection method based on a scalable attention residual CNN (SAR-CNN), which uses target detection to identify and locate diseases in chest X-rays and greatly improves work efficiency. We designed a multi-convolution feature fusion block (MFFB), tree-structured aggregation module (TSAM), and scalable channel and spatial attention (SCSA), which can effectively alleviate the difficulties in chest X-ray recognition caused by single resolution, weak communication of features of different layers, and lack of attention fusion, respectively. These three modules are embeddable and can be easily combined with other networks. Through a large number of experiments on the largest public lung chest radiograph detection dataset, VinDr-CXR, the mean average precision (mAP) of the proposed method was improved from 12.83% to 15.75% in the case of the PASCAL VOC 2010 standard, with IoU > 0.4, which exceeds the existing mainstream deep learning model. In addition, the proposed model has a lower complexity and faster reasoning speed, which is conducive to the implementation of computer-aided systems and provides referential solutions for relevant communities.
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Affiliation(s)
- Cong Lin
- College of Information and Communication Engineering, Hainan University, Haikou 570228, China
- College of Electronic and Information Engineering, Guangdong Ocean University, Zhanjiang 524088, China
| | - Yiquan Huang
- College of Electronic and Information Engineering, Guangdong Ocean University, Zhanjiang 524088, China
| | - Wenling Wang
- College of Information and Communication Engineering, Hainan University, Haikou 570228, China
| | - Siling Feng
- College of Information and Communication Engineering, Hainan University, Haikou 570228, China
| | - Siling Feng
- College of Information and Communication Engineering, Hainan University, Haikou 570228, China
- State Key Laboratory of Marine Resource Utilization in South China Sea, Hainan University, Haikou 570228, China
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81
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Deep SVDD and Transfer Learning for COVID-19 Diagnosis Using CT Images. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2023; 2023:6070970. [PMID: 36926185 PMCID: PMC10014155 DOI: 10.1155/2023/6070970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 01/25/2023] [Accepted: 02/06/2023] [Indexed: 03/09/2023]
Abstract
The novel coronavirus disease (COVID-19), which appeared in Wuhan, China, is spreading rapidly worldwide. Health systems in many countries have collapsed as a result of this pandemic, and hundreds of thousands of people have died due to acute respiratory distress syndrome caused by this virus. As a result, diagnosing COVID-19 in the early stages of infection is critical in the fight against the disease because it saves the patient's life and prevents the disease from spreading. In this study, we proposed a novel approach based on transfer learning and deep support vector data description (DSVDD) to distinguish among COVID-19, non-COVID-19 pneumonia, and intact CT images. Our approach consists of three models, each of which can classify one specific category as normal and the other as anomalous. To our knowledge, this is the first study to use the one-class DSVDD and transfer learning to diagnose lung disease. For the proposed approach, we used two scenarios: one with pretrained VGG16 and one with ResNet50. The proposed models were trained using data gathered with the assistance of an expert radiologist from three internet-accessible sources in end-to-end fusion using three split data ratios. Based on training with 70%, 50%, and 30% of the data, the proposed VGG16 models achieved (0.8281, 0.9170, and 0.9294) for the F1 score, while the proposed ResNet50 models achieved (0.9109, 0.9188, and 0.9333).
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82
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Hybrid intelligent model for classifying chest X-ray images of COVID-19 patients using genetic algorithm and neutrosophic logic. Soft comput 2023; 27:3427-3442. [PMID: 34421342 PMCID: PMC8371596 DOI: 10.1007/s00500-021-06103-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/30/2021] [Indexed: 12/23/2022]
Abstract
The highly spreading virus, COVID-19, created a huge need for an accurate and speedy diagnosis method. The famous RT-PCR test is costly and not available for many suspected cases. This article proposes a neurotrophic model to diagnose COVID-19 patients based on their chest X-ray images. The proposed model has five main phases. First, the speeded up robust features (SURF) method is applied to each X-ray image to extract robust invariant features. Second, three sampling algorithms are applied to treat imbalanced dataset. Third, the neutrosophic rule-based classification system is proposed to generate a set of rules based on the three neutrosophic values < T; I; F>, the degrees of truth, indeterminacy falsity. Fourth, a genetic algorithm is applied to select the optimal neutrosophic rules to improve the classification performance. Fifth, in this phase, the classification-based neutrosophic logic is proposed. The testing rule matrix is constructed with no class label, and the goal of this phase is to determine the class label for each testing rule using intersection percentage between testing and training rules. The proposed model is referred to as GNRCS. It is compared with six state-of-the-art classifiers such as multilayer perceptron (MLP), support vector machines (SVM), linear discriminant analysis (LDA), decision tree (DT), naive Bayes (NB), and random forest classifiers (RFC) with quality measures of accuracy, precision, sensitivity, specificity, and F1-score. The results show that the proposed model is powerful for COVID-19 recognition with high specificity and high sensitivity and less computational complexity. Therefore, the proposed GNRCS model could be used for real-time automatic early recognition of COVID-19.
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83
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Akl AA, Hosny KM, Fouda MM, Salah A. A hybrid CNN and ensemble model for COVID-19 lung infection detection on chest CT scans. PLoS One 2023; 18:e0282608. [PMID: 36893081 PMCID: PMC9997961 DOI: 10.1371/journal.pone.0282608] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 02/18/2023] [Indexed: 03/10/2023] Open
Abstract
COVID-19 is highly infectious and causes acute respiratory disease. Machine learning (ML) and deep learning (DL) models are vital in detecting disease from computerized chest tomography (CT) scans. The DL models outperformed the ML models. For COVID-19 detection from CT scan images, DL models are used as end-to-end models. Thus, the performance of the model is evaluated for the quality of the extracted feature and classification accuracy. There are four contributions included in this work. First, this research is motivated by studying the quality of the extracted feature from the DL by feeding these extracted to an ML model. In other words, we proposed comparing the end-to-end DL model performance against the approach of using DL for feature extraction and ML for the classification of COVID-19 CT scan images. Second, we proposed studying the effect of fusing extracted features from image descriptors, e.g., Scale-Invariant Feature Transform (SIFT), with extracted features from DL models. Third, we proposed a new Convolutional Neural Network (CNN) to be trained from scratch and then compared to the deep transfer learning on the same classification problem. Finally, we studied the performance gap between classic ML models against ensemble learning models. The proposed framework is evaluated using a CT dataset, where the obtained results are evaluated using five different metrics The obtained results revealed that using the proposed CNN model is better than using the well-known DL model for the purpose of feature extraction. Moreover, using a DL model for feature extraction and an ML model for the classification task achieved better results in comparison to using an end-to-end DL model for detecting COVID-19 CT scan images. Of note, the accuracy rate of the former method improved by using ensemble learning models instead of the classic ML models. The proposed method achieved the best accuracy rate of 99.39%.
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Affiliation(s)
- Ahmed A. Akl
- Senior Machine Learning Engineer, VA Computing, Cairo, Egypt
| | - Khalid M. Hosny
- Department of Information Technology, Faculty of Computers and Informatics, Zagazig University, Zagazig, Sharkia Egypt
- * E-mail:
| | - Mostafa M. Fouda
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID, United States of America
| | - Ahmad Salah
- Department of Computer Science, Faculty of Computers and Informatics, Zagazig University, Zagazig, Sharkia, Egypt
- Department of Information Technology, College of Computing and Information Sciences, University of Technology and Applied Sciences, Ibri, Al Dhahirah, Sultanate of Oman
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84
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Takateyama Y, Haruishi T, Hashimoto M, Otake Y, Akashi T, Shimizu A. Attention induction for a CT volume classification of COVID-19. Int J Comput Assist Radiol Surg 2023; 18:289-301. [PMID: 36251150 PMCID: PMC9574825 DOI: 10.1007/s11548-022-02769-y] [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: 03/15/2022] [Accepted: 09/29/2022] [Indexed: 02/03/2023]
Abstract
PURPOSE This study proposes a method to draw attention toward the specific radiological findings of coronavirus disease 2019 (COVID-19) in CT images, such as bilaterality of ground glass opacity (GGO) and/or consolidation, in order to improve the classification accuracy of input CT images. METHODS We propose an induction mask that combines a similarity and a bilateral mask. A similarity mask guides attention to regions with similar appearances, and a bilateral mask induces attention to the opposite side of the lung to capture bilaterally distributed lesions. An induction mask for pleural effusion is also proposed in this study. ResNet18 with nonlocal blocks was trained by minimizing the loss function defined by the induction mask. RESULTS The four-class classification accuracy of the CT images of 1504 cases was 0.6443, where class 1 was the typical appearance of COVID-19 pneumonia, class 2 was the indeterminate appearance of COVID-19 pneumonia, class 3 was the atypical appearance of COVID-19 pneumonia, and class 4 was negative for pneumonia. The four classes were divided into two subgroups. The accuracy of COVID-19 and pneumonia classifications was evaluated, which were 0.8205 and 0.8604, respectively. The accuracy of the four-class and COVID-19 classifications improved when attention was paid to pleural effusion. CONCLUSION The proposed attention induction method was effective for the classification of CT images of COVID-19 patients. Improvement of the classification accuracy of class 3 by focusing on features specific to the class remains a topic for future work.
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Affiliation(s)
- Yusuke Takateyama
- grid.136594.c0000 0001 0689 5974Institute of Engineering, Tokyo University of Agriculture and Technology, Koganei, Tokyo, Japan
| | - Takahito Haruishi
- grid.136594.c0000 0001 0689 5974Institute of Engineering, Tokyo University of Agriculture and Technology, Koganei, Tokyo, Japan
| | - Masahiro Hashimoto
- grid.26091.3c0000 0004 1936 9959Department of Radiology, Keio University school of Medicine, Shinjuku-ku, Tokyo, Japan
| | - Yoshito Otake
- grid.260493.a0000 0000 9227 2257Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma-shi, Nara, Japan
| | - Toshiaki Akashi
- grid.258269.20000 0004 1762 2738Department of Radiology, Juntendo University, Bunkyo-ku, Tokyo, Japan
| | - Akinobu Shimizu
- grid.136594.c0000 0001 0689 5974Institute of Engineering, Tokyo University of Agriculture and Technology, Koganei, Tokyo, Japan
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85
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Marefat A, Marefat M, Hassannataj Joloudari J, Nematollahi MA, Lashgari R. CCTCOVID: COVID-19 detection from chest X-ray images using Compact Convolutional Transformers. Front Public Health 2023; 11:1025746. [PMID: 36923036 PMCID: PMC10009152 DOI: 10.3389/fpubh.2023.1025746] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Accepted: 02/07/2023] [Indexed: 03/03/2023] Open
Abstract
COVID-19 is a novel virus that attacks the upper respiratory tract and the lungs. Its person-to-person transmissibility is considerably rapid and this has caused serious problems in approximately every facet of individuals' lives. While some infected individuals may remain completely asymptomatic, others have been frequently witnessed to have mild to severe symptoms. In addition to this, thousands of death cases around the globe indicated that detecting COVID-19 is an urgent demand in the communities. Practically, this is prominently done with the help of screening medical images such as Computed Tomography (CT) and X-ray images. However, the cumbersome clinical procedures and a large number of daily cases have imposed great challenges on medical practitioners. Deep Learning-based approaches have demonstrated a profound potential in a wide range of medical tasks. As a result, we introduce a transformer-based method for automatically detecting COVID-19 from X-ray images using Compact Convolutional Transformers (CCT). Our extensive experiments prove the efficacy of the proposed method with an accuracy of 99.22% which outperforms the previous works.
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Affiliation(s)
- Abdolreza Marefat
- Department of Computer Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran
| | - Mahdieh Marefat
- Department of Cellular and Molecular Biology, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | | | | | - Reza Lashgari
- Institute of Medical Science and Technology, Shahid Beheshti University, Tehran, Iran
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86
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Kumar A, Misra SC, Chan FTS. Leveraging AI for advanced analytics to forecast altered tourism industry parameters: A COVID-19 motivated study. EXPERT SYSTEMS WITH APPLICATIONS 2022; 210:118628. [PMID: 36032358 PMCID: PMC9394102 DOI: 10.1016/j.eswa.2022.118628] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 08/16/2022] [Accepted: 08/16/2022] [Indexed: 06/15/2023]
Abstract
COVID-19 pandemic has given a sudden shock to economy indices worldwide and especially to the tourism sector, which is already very sensitive to such crises as natural calamities, terrorist activities, virus outbreaks and unwanted conditions. The economic implications for a reduction in tourism demand, and the need to analyse post-COVID-19 tourism motivates our research. This study aims to forecast the future trends for foreign tourist arrivals and foreign exchange earnings for India and to formulate a model to predict the future trends based on the COVID-19 parameters, vaccinations and stringency index (Government travelling guidelines). In the study, we have developed artificial intelligence models (random forest, linear regression) using the stacked based ensemble learning method for the development of base models and meta models for the study of COVID-19 and its effect on the tourism industry. The architecture of a stacking model consists of two or more base models, often referred to as level-0 models, and a meta-model that combines the predictions of the base models, and is referred to as a level-1 model (Smyth & Wolpert, 1999). The results show that the projected losses require quick action on developing new practices to sustain and complement the resilience of tourism per se.
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Key Words
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H
x
, Level 1 regressor based on the level 0 regressor ht
- Artificial Intelligence
- COVID-19
- D, Training Data Set
- Foreign Tourist Arrivals
- N, Dataset of Labels
- RM, Input dataset of M number, where M is a finite real number
- Random forest model
- Tourism industry
- ht, base regressor of t number of training data points
- i,j, finite real numbers
- xi, x1, x2, x3…….. where xi is a input dataset
- yi, y1, y2, y3……. where yi is a output dataset label
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Affiliation(s)
- Ankur Kumar
- Industrial Management Engineering Indian Institute of Technology, Kanpur, Kanpur, Uttar Pradesh, India
| | - Subhas Chandra Misra
- Industrial Management Engineering Indian Institute of Technology, Kanpur, Kanpur, Uttar Pradesh, India
| | - Felix T S Chan
- Department of Decision Sciences, Macau University of Science and Technology, Taipa, Macao
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87
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Bhatele KR, Jha A, Tiwari D, Bhatele M, Sharma S, Mithora MR, Singhal S. COVID-19 Detection: A Systematic Review of Machine and Deep Learning-Based Approaches Utilizing Chest X-Rays and CT Scans. Cognit Comput 2022:1-38. [PMID: 36593991 PMCID: PMC9797382 DOI: 10.1007/s12559-022-10076-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 11/15/2022] [Indexed: 12/30/2022]
Abstract
This review study presents the state-of-the-art machine and deep learning-based COVID-19 detection approaches utilizing the chest X-rays or computed tomography (CT) scans. This study aims to systematically scrutinize as well as to discourse challenges and limitations of the existing state-of-the-art research published in this domain from March 2020 to August 2021. This study also presents a comparative analysis of the performance of four majorly used deep transfer learning (DTL) models like VGG16, VGG19, ResNet50, and DenseNet over the COVID-19 local CT scans dataset and global chest X-ray dataset. A brief illustration of the majorly used chest X-ray and CT scan datasets of COVID-19 patients utilized in state-of-the-art COVID-19 detection approaches are also presented for future research. The research databases like IEEE Xplore, PubMed, and Web of Science are searched exhaustively for carrying out this survey. For the comparison analysis, four deep transfer learning models like VGG16, VGG19, ResNet50, and DenseNet are initially fine-tuned and trained using the augmented local CT scans and global chest X-ray dataset in order to observe their performance. This review study summarizes major findings like AI technique employed, type of classification performed, used datasets, results in terms of accuracy, specificity, sensitivity, F1 score, etc., along with the limitations, and future work for COVID-19 detection in tabular manner for conciseness. The performance analysis of the four majorly used deep transfer learning models affirms that Visual Geometry Group 19 (VGG19) model delivered the best performance over both COVID-19 local CT scans dataset and global chest X-ray dataset.
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Affiliation(s)
| | - Anand Jha
- RJIT BSF Academy, Tekanpur, Gwalior India
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88
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Wu H, Zhao HJ, Xue WL, Wang YC, Zhang WY, Wang XL. Ultrasound and elastography role in pre- and post-operative evaluation of median neuropathy in patients with carpal tunnel syndrome. Front Neurol 2022; 13:1079737. [PMID: 36588903 PMCID: PMC9800593 DOI: 10.3389/fneur.2022.1079737] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 11/23/2022] [Indexed: 12/23/2022] Open
Abstract
Introduction Carpal tunnel syndrome (CTS) is a common compression neuropathy of the median nerve in the wrist. Early diagnosis of CTS is essential for selecting treatment options and assessing prognosis. The current diagnosis of CTS is based on the patient's clinical symptoms, signs, and an electromyography (EMG) test. However, they have some limitations. Recently, ultrasound has been adopted as an adjunct diagnostic tool for electromyography (EMG). Ultrasound is a non-invasive and cost-effective technique. It provides a dynamic display of morphological changes in the median nerve and an assessment of CTS etiology such as tenosynovitis, mass compression, and tendon disease. This study aimed to investigate the value of conventional ultrasound and real-time shear wave elastography (SWE) in evaluation of median neuropathy in patients with carpal tunnel syndrome (CTS) before and after surgery. Methods First, the Boston Carpal Tunnel Questionnaire (BCTQ) was administered to patients with CTS. All subjects were measured at three levels: the distal 1/3 of the forearm, the carpal tunnel inlet, and the distal carpal tunnel using conventional ultrasound and SWE. Median nerve parameters were examined in patients with CTS 1 week after surgery. Results The cross-sectional area (CSA) and stiffness of the median nerve at the carpal tunnel inlet and distal carpal tunnel were significantly higher in patients with CTS than in healthy controls (p < 0.001). The CSA and stiffness of the median nerve at the carpal tunnel inlet were statistically significantly significantly between pre- and postoperative patients with CTS (p < 0.001). The CSA and stiffness of the nerve in patients with CTS had a positive correlation with electrophysiology severity. Conclusions and discussion Conventional ultrasound and elastography are valuable in the diagnosis of CTS and are useful in the clinical assessment of patient's nerve recovery after operation.
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89
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Uddin KMM, Dey SK, Babu HMH, Mostafiz R, Uddin S, Shoombuatong W, Moni MA. Feature fusion based VGGFusionNet model to detect COVID-19 patients utilizing computed tomography scan images. Sci Rep 2022; 12:21796. [PMID: 36526680 PMCID: PMC9757637 DOI: 10.1038/s41598-022-25539-x] [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: 06/13/2022] [Accepted: 11/30/2022] [Indexed: 12/23/2022] Open
Abstract
COVID-19 is one of the most life-threatening and dangerous diseases caused by the novel Coronavirus, which has already afflicted a larger human community worldwide. This pandemic disease recovery is possible if detected in the early stage. We proposed an automated deep learning approach from Computed Tomography (CT) scan images to detect COVID-19 positive patients by following a four-phase paradigm for COVID-19 detection: preprocess the CT scan images; remove noise from test image by using anisotropic diffusion techniques; make a different segment for the preprocessed images; and train and test COVID-19 detection using Convolutional Neural Network (CNN) models. This study employed well-known pre-trained models, including AlexNet, ResNet50, VGG16 and VGG19 to evaluate experiments. 80% of images are used to train the network in the detection process, while the remaining 20% are used to test it. The result of the experiment evaluation confirmed that the VGG19 pre-trained CNN model achieved better accuracy (98.06%). We used 4861 real-life COVID-19 CT images for experiment purposes, including 3068 positive and 1793 negative images. These images were acquired from a hospital in Sao Paulo, Brazil and two other different data sources. Our proposed method revealed very high accuracy and, therefore, can be used as an assistant to help professionals detect COVID-19 patients accurately.
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Affiliation(s)
| | - Samrat Kumar Dey
- grid.443070.4School of Science and Technology, Bangladesh Open University, Gazipur, 1705 Bangladesh
| | - Hafiz Md. Hasan Babu
- grid.8198.80000 0001 1498 6059Department of Computer Science and Engineering, University of Dhaka, Dhaka, 1000 Bangladesh
| | - Rafid Mostafiz
- grid.449503.f0000 0004 1798 7083Institute of Information Technology, Noakhali Science and Technology University, Noakhali, 3814 Bangladesh
| | - Shahadat Uddin
- grid.1013.30000 0004 1936 834XSchool of Project Management, Faculty of Engineering, The University of Sydney, Forest Lodge, NSW 2037 Australia
| | - Watshara Shoombuatong
- grid.10223.320000 0004 1937 0490Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700 Thailand
| | - Mohammad Ali Moni
- grid.1003.20000 0000 9320 7537Artificial Intelligence and Digital Health Data Science, School of Health and Rehabilitation Sciences, Faculty of Health and Behavioural Sciences, The University of Queensland, St Lucia, QLD 4072 Australia
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90
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Aslan MF. A robust semantic lung segmentation study for CNN-based COVID-19 diagnosis. CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS : AN INTERNATIONAL JOURNAL SPONSORED BY THE CHEMOMETRICS SOCIETY 2022; 231:104695. [PMID: 36311473 PMCID: PMC9595502 DOI: 10.1016/j.chemolab.2022.104695] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 10/16/2022] [Accepted: 10/17/2022] [Indexed: 05/06/2023]
Abstract
This paper aims to diagnose COVID-19 by using Chest X-Ray (CXR) scan images in a deep learning-based system. First of all, COVID-19 Chest X-Ray Dataset is used to segment the lung parts in CXR images semantically. DeepLabV3+ architecture is trained by using the masks of the lung parts in this dataset. The trained architecture is then fed with images in the COVID-19 Radiography Database. In order to improve the output images, some image preprocessing steps are applied. As a result, lung regions are successfully segmented from CXR images. The next step is feature extraction and classification. While features are extracted with modified AlexNet (mAlexNet), Support Vector Machine (SVM) is used for classification. As a result, 3-class data consisting of Normal, Viral Pneumonia and COVID-19 class are classified with 99.8% success. Classification results show that the proposed method is superior to previous state-of-the-art methods.
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Affiliation(s)
- Muhammet Fatih Aslan
- Electrical and Electronics Engineering, Karamanoglu Mehmetbey University, Karaman, Turkey
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91
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Guo Y, Li X, Chen D, Zhang H. Evaluation Study on the Use of Non-Contact Prevention and Protection Products in the Context of COVID-19: A Comprehensive Evaluation Method from AHP and Entropy Weight Method. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:16857. [PMID: 36554734 PMCID: PMC9778662 DOI: 10.3390/ijerph192416857] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Revised: 11/29/2022] [Accepted: 12/13/2022] [Indexed: 06/17/2023]
Abstract
In the post-epidemic era, there is an endless supply of epidemic prevention products that cover a wide range of public areas. The introduction of such products has eased the tense pattern of virus proliferation in the context of the epidemic, and effectively demonstrated the initiatives implemented by the Chinese people in response to the outbreak. This paper therefore begins with the study of contactless epidemic prevention products, which appear in a form that meets the needs of contemporary society and offers a new mode of living to it. It enriches the measures for epidemic prevention and control. By obtaining satisfaction ratings from the user community, the performance of such products can be understood in time to provide a substantial basis for the subsequent upgrading and optimization or transformation of such products. This study uses the KJ method and questionnaires to construct an index system for contactless epidemic prevention products, grasp users' needs for epidemic prevention products in real time, classify and identify such products, and select such products as epidemic prevention smart security gates, medical delivery robots, infrared handheld thermometers, thermographic body temperature screening, contactless inductive lift buttons, and contactless medical vending machines. The questionnaire was designed with four dimensions: safety, intelligence, aesthetics and economy. A sample size of 262 was collected through the distribution of questionnaires. We used AHP and entropy weighting methods for the comprehensive evaluation; AHP basically tells us how satisfied most users are with this type of product. The use of the entropy weighting method can achieve objectivity in the weighting process. Combining the two approaches helps to improve the scientific nature of the weighting of the evaluation indexes for contactless and epidemic-proof products. It is clear from the AHP analysis that, firstly, there are differences in the perceptions of the performance of this type of product between different age groups. Secondly, the user group rated the perceived performance of the product presented as high (Bn>0.200), which users can subjectively and directly perceive. Next, the perceived future sustainable economic development of this product category is low (Bn≤0.200), and users place low importance on its economic aspects as an objective additional condition. The entropy method of analysis shows that, under reasonable government control of the market for intelligent products, the safety, intelligence and aesthetic effects of these products are significant (Cm≤0.100); further, the economic presentation of these products has yet to be optimized and upgraded (Cm>0.100).
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Affiliation(s)
- Yanlong Guo
- Social Innovation Design Research Centre, Department of Design, Anhui University, Hefei 203106, China
- Anhui Institute of Contemporary Studies, Anhui Academy of Social Sciences, Hefei 203106, China
| | - Xuan Li
- Social Innovation Design Research Centre, Department of Design, Anhui University, Hefei 203106, China
| | - Denghang Chen
- Department of Science and Technology Communication, University of Science and Technology of China, Hefei 203106, China
| | - Han Zhang
- College of Environmental Science and Engineering, Ocean University of China, Qingdao 266000, China
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92
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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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93
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Celik Y, Aslan MF, Sabanci K, Stuart S, Woo WL, Godfrey A. Improving Inertial Sensor-Based Activity Recognition in Neurological Populations. SENSORS (BASEL, SWITZERLAND) 2022; 22:9891. [PMID: 36560259 PMCID: PMC9783358 DOI: 10.3390/s22249891] [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: 11/24/2022] [Revised: 12/14/2022] [Accepted: 12/14/2022] [Indexed: 06/17/2023]
Abstract
Inertial sensor-based human activity recognition (HAR) has a range of healthcare applications as it can indicate the overall health status or functional capabilities of people with impaired mobility. Typically, artificial intelligence models achieve high recognition accuracies when trained with rich and diverse inertial datasets. However, obtaining such datasets may not be feasible in neurological populations due to, e.g., impaired patient mobility to perform many daily activities. This study proposes a novel framework to overcome the challenge of creating rich and diverse datasets for HAR in neurological populations. The framework produces images from numerical inertial time-series data (initial state) and then artificially augments the number of produced images (enhanced state) to achieve a larger dataset. Here, we used convolutional neural network (CNN) architectures by utilizing image input. In addition, CNN enables transfer learning which enables limited datasets to benefit from models that are trained with big data. Initially, two benchmarked public datasets were used to verify the framework. Afterward, the approach was tested in limited local datasets of healthy subjects (HS), Parkinson's disease (PD) population, and stroke survivors (SS) to further investigate validity. The experimental results show that when data augmentation is applied, recognition accuracies have been increased in HS, SS, and PD by 25.6%, 21.4%, and 5.8%, respectively, compared to the no data augmentation state. In addition, data augmentation contributes to better detection of stair ascent and stair descent by 39.1% and 18.0%, respectively, in limited local datasets. Findings also suggest that CNN architectures that have a small number of deep layers can achieve high accuracy. The implication of this study has the potential to reduce the burden on participants and researchers where limited datasets are accrued.
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Affiliation(s)
- Yunus Celik
- Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne NE1 8ST, UK
| | - M. Fatih Aslan
- Department of Electrical and Electronics Engineering, Karamanoglu Mehmetbey University, Karaman 70100, Turkey
| | - Kadir Sabanci
- Department of Electrical and Electronics Engineering, Karamanoglu Mehmetbey University, Karaman 70100, Turkey
| | - Sam Stuart
- Department of Sport, Exercise and Rehabilitation, Northumbria University, Newcastle upon Tyne NE1 8ST, UK
| | - Wai Lok Woo
- Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne NE1 8ST, UK
| | - Alan Godfrey
- Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne NE1 8ST, UK
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94
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Phung KA, Nguyen TT, Wangad N, Baraheem S, Vo ND, Nguyen K. Disease Recognition in X-ray Images with Doctor Consultation-Inspired Model. J Imaging 2022; 8:jimaging8120323. [PMID: 36547488 PMCID: PMC9786084 DOI: 10.3390/jimaging8120323] [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: 08/25/2022] [Revised: 11/24/2022] [Accepted: 11/30/2022] [Indexed: 12/12/2022] Open
Abstract
The application of chest X-ray imaging for early disease screening is attracting interest from the computer vision and deep learning community. To date, various deep learning models have been applied in X-ray image analysis. However, models perform inconsistently depending on the dataset. In this paper, we consider each individual model as a medical doctor. We then propose a doctor consultation-inspired method that fuses multiple models. In particular, we consider both early and late fusion mechanisms for consultation. The early fusion mechanism combines the deep learned features from multiple models, whereas the late fusion method combines the confidence scores of all individual models. Experiments on two X-ray imaging datasets demonstrate the superiority of the proposed method relative to baseline. The experimental results also show that early consultation consistently outperforms the late consultation mechanism in both benchmark datasets. In particular, the early doctor consultation-inspired model outperforms all individual models by a large margin, i.e., 3.03 and 1.86 in terms of accuracy in the UIT COVID-19 and chest X-ray datasets, respectively.
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Affiliation(s)
- Kim Anh Phung
- Department of Computer Science, University of Dayton, Dayton, OH 45469, USA
| | - Thuan Trong Nguyen
- Faculty of Software Engineering, University of Information Technology, Linh Trung Ward, Thu Duc District, Ho Chi Minh City 70000, Vietnam
| | - Nileshkumar Wangad
- Department of Computer Science, University of Dayton, Dayton, OH 45469, USA
| | - Samah Baraheem
- Department of Computer Science, University of Dayton, Dayton, OH 45469, USA
| | - Nguyen D. Vo
- Faculty of Software Engineering, University of Information Technology, Linh Trung Ward, Thu Duc District, Ho Chi Minh City 70000, Vietnam
| | - Khang Nguyen
- Faculty of Software Engineering, University of Information Technology, Linh Trung Ward, Thu Duc District, Ho Chi Minh City 70000, Vietnam
- Correspondence:
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95
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Dual_Pachi: Attention-based dual path framework with intermediate second order-pooling for Covid-19 detection from chest X-ray images. Comput Biol Med 2022; 151:106324. [PMID: 36423531 PMCID: PMC9671873 DOI: 10.1016/j.compbiomed.2022.106324] [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: 09/07/2022] [Revised: 10/27/2022] [Accepted: 11/14/2022] [Indexed: 11/19/2022]
Abstract
Numerous machine learning and image processing algorithms, most recently deep learning, allow the recognition and classification of COVID-19 disease in medical images. However, feature extraction, or the semantic gap between low-level visual information collected by imaging modalities and high-level semantics, is the fundamental shortcoming of these techniques. On the other hand, several techniques focused on the first-order feature extraction of the chest X-Ray thus making the employed models less accurate and robust. This study presents Dual_Pachi: Attention Based Dual Path Framework with Intermediate Second Order-Pooling for more accurate and robust Chest X-ray feature extraction for Covid-19 detection. Dual_Pachi consists of 4 main building Blocks; Block one converts the received chest X-Ray image to CIE LAB coordinates (L & AB channels which are separated at the first three layers of a modified Inception V3 Architecture.). Block two further exploit the global features extracted from block one via a global second-order pooling while block three focuses on the low-level visual information and the high-level semantics of Chest X-ray image features using a multi-head self-attention and an MLP Layer without sacrificing performance. Finally, the fourth block is the classification block where classification is done using fully connected layers and SoftMax activation. Dual_Pachi is designed and trained in an end-to-end manner. According to the results, Dual_Pachi outperforms traditional deep learning models and other state-of-the-art approaches described in the literature with an accuracy of 0.96656 (Data_A) and 0.97867 (Data_B) for the Dual_Pachi approach and an accuracy of 0.95987 (Data_A) and 0.968 (Data_B) for the Dual_Pachi without attention block model. A Grad-CAM-based visualization is also built to highlight where the applied attention mechanism is concentrated.
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96
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Improve the cytotoxic effects of megavoltage radiation treatment by Fe3O4@Cus–PEG nanoparticles as a novel radiosensitizer in colorectal cancer cells. Cancer Nanotechnol 2022. [DOI: 10.1186/s12645-022-00131-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
To enhance the performance of radiotherapy, emerging nanoparticles that can professionally enhance X-ray irradiation to destruct cancer cells are extremely necessary. Here, we examined the potential of PEG-coated magnetite copper sulfide hetero-nanoparticles (Fe3O4@Cus–PEG) as a radiosensitizer agent.
Methods
Fe3O4@Cus–PEG nanoparticles were synthesized and characterized. The toxicity of nanoparticles on HT-29 colorectal cancer cells was assessed by the MTT assay. The radio-sensitizing effects of Fe3O4@Cus–PEG nanoparticles on HT-29 cancer cells were investigated by the MTT and colony formation assays. Moreover, the underlying mechanisms for Fe3O4@Cus–PEG nanoparticles to improve the radiation sensitivity of cells were evaluated.
Results
The results demonstrated that nanoparticles enhanced the effects of X-ray irradiation in a dose-dependent manner. The effects of combined treatments (nanoparticles and X-ray radiation) were strongly synergistic. The sensitizing enhancement ratio (SER) of nanoparticles was 2.02. Our in vitro assays demonstrated that the nitric oxide production, the intracellular hydrogen peroxide concentration, and the expression level of Bax and Caspase-3 genes significantly increased in the cells treated with the combination of nanoparticles and radiation. Whereas, the Glutathione peroxidase enzyme activity and the expression level of the Bcl-2 gene in the combined treatment significantly decreased compared to the radiation alone.
Conclusions
Our study suggests that Fe3O4@Cus–PEG nanoparticles are the promising nano radio-sensitizing agents for the treatment of cancer cells to enhance the efficacy of radiation therapy through increasing the reactive oxygen species generation, nitric oxide production, and inducing apoptosis.
Graphical Abstract
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97
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Duong LT, Nguyen PT, Iovino L, Flammini M. Automatic detection of Covid-19 from chest X-ray and lung computed tomography images using deep neural networks and transfer learning. Appl Soft Comput 2022; 132:109851. [PMCID: PMC9686054 DOI: 10.1016/j.asoc.2022.109851] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Revised: 10/02/2022] [Accepted: 11/14/2022] [Indexed: 11/27/2022]
Abstract
The world has been undergoing the most ever unprecedented circumstances caused by the coronavirus pandemic, which is having a devastating global effect in different aspects of life. Since there are not effective antiviral treatments for Covid-19 yet, it is crucial to early detect and monitor the progression of the disease, thereby helping to reduce mortality. While different measures are being used to combat the virus, medical imaging techniques have been examined to support doctors in diagnosing the disease. In this paper, we present a practical solution for the detection of Covid-19 from chest X-ray (CXR) and lung computed tomography (LCT) images, exploiting cutting-edge Machine Learning techniques. As the main classification engine, we make use of EfficientNet and MixNet, two recently developed families of deep neural networks. Furthermore, to make the training more effective and efficient, we apply three transfer learning algorithms. The ultimate aim is to build a reliable expert system to detect Covid-19 from different sources of images, making it be a multi-purpose AI diagnosing system. We validated our proposed approach using four real-world datasets. The first two are CXR datasets consist of 15,000 and 17,905 images, respectively. The other two are LCT datasets with 2,482 and 411,528 images, respectively. The five-fold cross-validation methodology was used to evaluate the approach, where the dataset is split into five parts, and accordingly the evaluation is conducted in five rounds. By each evaluation, four parts are combined to form the training data, and the remaining one is used for testing. We obtained an encouraging prediction performance for all the considered datasets. In all the configurations, the obtained accuracy is always larger than 95.0%. Compared to various existing studies, our approach yields a substantial performance gain. Moreover, such an improvement is statistically significant.
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Affiliation(s)
- Linh T. Duong
- Institute of Research and Development, Duy Tan University, Viet Nam
| | - Phuong T. Nguyen
- Department of Information Engineering, Computer Science and Mathematics University of L’Aquila, Italy,Corresponding author
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98
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Kadhim YA, Khan MU, Mishra A. Deep Learning-Based Computer-Aided Diagnosis (CAD): Applications for Medical Image Datasets. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22228999. [PMID: 36433595 PMCID: PMC9692938 DOI: 10.3390/s22228999] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/23/2022] [Revised: 11/17/2022] [Accepted: 11/18/2022] [Indexed: 05/26/2023]
Abstract
Computer-aided diagnosis (CAD) has proved to be an effective and accurate method for diagnostic prediction over the years. This article focuses on the development of an automated CAD system with the intent to perform diagnosis as accurately as possible. Deep learning methods have been able to produce impressive results on medical image datasets. This study employs deep learning methods in conjunction with meta-heuristic algorithms and supervised machine-learning algorithms to perform an accurate diagnosis. Pre-trained convolutional neural networks (CNNs) or auto-encoder are used for feature extraction, whereas feature selection is performed using an ant colony optimization (ACO) algorithm. Ant colony optimization helps to search for the best optimal features while reducing the amount of data. Lastly, diagnosis prediction (classification) is achieved using learnable classifiers. The novel framework for the extraction and selection of features is based on deep learning, auto-encoder, and ACO. The performance of the proposed approach is evaluated using two medical image datasets: chest X-ray (CXR) and magnetic resonance imaging (MRI) for the prediction of the existence of COVID-19 and brain tumors. Accuracy is used as the main measure to compare the performance of the proposed approach with existing state-of-the-art methods. The proposed system achieves an average accuracy of 99.61% and 99.18%, outperforming all other methods in diagnosing the presence of COVID-19 and brain tumors, respectively. Based on the achieved results, it can be claimed that physicians or radiologists can confidently utilize the proposed approach for diagnosing COVID-19 patients and patients with specific brain tumors.
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Affiliation(s)
- Yezi Ali Kadhim
- Department of Modeling and Design of Engineering Systems (MODES), Atilim University, Ankara 06830, Turkey
- Department of Electrical and Electronics Engineering, Atilim University, Incek, Ankara 06830, Turkey
| | - Muhammad Umer Khan
- Department of Mechatronics Engineering, Atilim University, Incek, Ankara 06830, Turkey
| | - Alok Mishra
- Department of Software Engineering, Atilim University, Incek, Ankara 06830, Turkey
- Informatics and Digitalization Group, Molde University College—Specialized University in Logistics, 6410 Molde, Norway
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99
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Dubey AK, Mohbey KK. Combined Cloud-Based Inference System for the Classification of COVID-19 in CT-Scan and X-Ray Images. NEW GENERATION COMPUTING 2022; 41:61-84. [PMID: 36439302 PMCID: PMC9676871 DOI: 10.1007/s00354-022-00195-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Accepted: 11/09/2022] [Indexed: 06/16/2023]
Abstract
In the past few years, most of the work has been done around the classification of covid-19 using different images like CT-scan, X-ray, and ultrasound. But none of that is capable enough to deal with each of these image types on a single common platform and can identify the possibility that a person is suffering from COVID or not. Thus, we realized there should be a platform to identify COVID-19 in CT-scan and X-ray images on the fly. So, to fulfill this need, we proposed an AI model to identify CT-scan and X-ray images from each other and then use this inference to classify them of COVID positive or negative. The proposed model uses the inception architecture under the hood and trains on the open-source extended covid-19 dataset. The dataset consists of plenty of images for both image types and is of size 4 GB. We achieved an accuracy of 100%, average macro-Precision of 100%, average macro-Recall of 100%, average macro f1-score of 100%, and AUC score of 99.6%. Furthermore, in this work, cloud-based architecture is proposed to massively scale and load balance as the Number of user requests rises. As a result, it will deliver a service with minimal latency to all users.
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Affiliation(s)
- Ankit Kumar Dubey
- Department of Computer Science, Central University of Rajasthan, Ajmer, India
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100
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Dey S, Bhattacharya R, Malakar S, Schwenker F, Sarkar R. CovidConvLSTM: A fuzzy ensemble model for COVID-19 detection from chest X-rays. EXPERT SYSTEMS WITH APPLICATIONS 2022; 206:117812. [PMID: 35754941 PMCID: PMC9212804 DOI: 10.1016/j.eswa.2022.117812] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Revised: 06/05/2022] [Accepted: 06/06/2022] [Indexed: 05/17/2023]
Abstract
The rapid outbreak of COVID-19 has affected the lives and livelihoods of a large part of the society. Hence, to confine the rapid spread of this virus, early detection of COVID-19 is extremely important. One of the most common ways of detecting COVID-19 is by using chest X-ray images. In the literature, it is found that most of the research activities applied convolutional neural network (CNN) models where the features generated by the last convolutional layer were directly passed to the classification models. In this paper, convolutional long short-term memory (ConvLSTM) layer is used in order to encode the spatial dependency among the feature maps obtained from the last convolutional layer of the CNN and to improve the image representational capability of the model. Additionally, the squeeze-and-excitation (SE) block, a spatial attention mechanism, is used to allocate weights to important local features. These two mechanisms are employed on three popular CNN models - VGG19, InceptionV3, and MobileNet to improve their classification strength. Finally, the Sugeno fuzzy integral based ensemble method is used on these classifiers' outputs to enhance the detection accuracy further. For experiments, three chest X-ray datasets, which are very prevalent for COVID-19 detection, are considered. For all the three datasets, it is found that the results obtained by the proposed method are comparable to state-of-the-art methods. The code, along with the pre-trained models, can be found at https://github.com/colabpro123/CovidConvLSTM.
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Affiliation(s)
- Subhrajit Dey
- Department of Electrical Engineering, Jadavpur University, Kolkata, India
| | - Rajdeep Bhattacharya
- Department of Computer Science and Engineering, Jadavpur University, Kolkata, India
| | - Samir Malakar
- Department of Computer Science, Asutosh College, Kolkata, India
| | | | - Ram Sarkar
- Department of Computer Science and Engineering, Jadavpur University, Kolkata, India
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