{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T06:42:46Z","timestamp":1769755366160,"version":"3.49.0"},"reference-count":85,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2021,8,24]],"date-time":"2021-08-24T00:00:00Z","timestamp":1629763200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The global COVID-19 pandemic that started in 2019 and created major disruptions around the world demonstrated the imperative need for quick, inexpensive, accessible and reliable diagnostic methods that would allow the detection of infected individuals with minimal resources. Radiography, and more specifically, chest radiography, is a relatively inexpensive medical imaging modality that can potentially offer a solution for the diagnosis of COVID-19 cases. In this work, we examined eleven deep convolutional neural network architectures for the task of classifying chest X-ray images as belonging to healthy individuals, individuals with COVID-19 or individuals with viral pneumonia. All the examined networks are established architectures that have been proven to be efficient in image classification tasks, and we evaluated three different adjustments to modify the architectures for the task at hand by expanding them with additional layers. The proposed approaches were evaluated for all the examined architectures on a dataset with real chest X-ray images, reaching the highest classification accuracy of 98.04% and the highest F1-score of 98.22% for the best-performing setting.<\/jats:p>","DOI":"10.3390\/s21175702","type":"journal-article","created":{"date-parts":[[2021,8,24]],"date-time":"2021-08-24T22:09:39Z","timestamp":1629842979000},"page":"5702","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["On the Use of Deep Learning for Imaging-Based COVID-19 Detection Using Chest X-rays"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1624-6668","authenticated-orcid":false,"given":"Gabriel Iluebe","family":"Okolo","sequence":"first","affiliation":[{"name":"School of Computing, Engineering and Physical Sciences, University of the West of Scotland, Paisley PA1 2BE, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9190-0941","authenticated-orcid":false,"given":"Stamos","family":"Katsigiannis","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Durham University, Durham DH1 3LE, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6674-7890","authenticated-orcid":false,"given":"Turke","family":"Althobaiti","sequence":"additional","affiliation":[{"name":"Faculty of Science, Northern Border University, Arar 91431, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5088-1462","authenticated-orcid":false,"given":"Naeem","family":"Ramzan","sequence":"additional","affiliation":[{"name":"School of Computing, Engineering and Physical Sciences, University of the West of Scotland, Paisley PA1 2BE, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,24]]},"reference":[{"key":"ref_1","first-page":"256","article-title":"Real-time forecasts of the COVID-19 epidemic in China from February 5th to February 24th, 2020","volume":"5","author":"Roosa","year":"2020","journal-title":"Infect. Dis. Model."},{"key":"ref_2","first-page":"2000094","article-title":"First cases of coronavirus disease 2019 (COVID-19) in France: Surveillance, investigations and control measures, January 2020","volume":"25","author":"Stoecklin","year":"2020","journal-title":"Eurosurveillance"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1708","DOI":"10.1056\/NEJMoa2002032","article-title":"Clinical characteristics of coronavirus disease 2019 in China","volume":"382","author":"Guan","year":"2020","journal-title":"N. Engl. J. Med."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1239","DOI":"10.1001\/jama.2020.2648","article-title":"Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: Summary of a report of 72314 cases from the Chinese Center for Disease Control and Prevention","volume":"323","author":"Wu","year":"2020","journal-title":"JAMA"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"147","DOI":"10.7326\/M20-1382","article-title":"Chest Computed Tomography for Detection of Coronavirus Disease 2019 (COVID-19): Don\u2019t Rush the Science","volume":"173","author":"Hope","year":"2020","journal-title":"Ann. Intern. Med."},{"key":"ref_6","unstructured":"World Health Organization (2020). Clinical Management of Severe Acute Respiratory Infection when Novel Coronavirus (2019-nCoV) Infection Is Suspected: Interim Guidance, 28 January 2020, World Health Organization. Technical Documents."},{"key":"ref_7","first-page":"1843","article-title":"Detection of SARS-CoV-2 in different types of clinical specimens","volume":"323","author":"Wang","year":"2020","journal-title":"JAMA"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"e115","DOI":"10.1148\/radiol.2020200432","article-title":"Sensitivity of Chest CT for COVID-19: Comparison to RT-PCR","volume":"296","author":"Fang","year":"2020","journal-title":"Radiology"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.ijid.2020.04.023","article-title":"Comparison of nasopharyngeal and oropharyngeal swabs for SARS-CoV-2 detection in 353 patients received tests with both specimens simultaneously","volume":"94","author":"Wang","year":"2020","journal-title":"Int. J. Infect. Dis."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2000568","DOI":"10.2807\/1560-7917.ES.2020.25.50.2000568","article-title":"Estimating the false-negative test probability of SARS-CoV-2 by RT-PCR","volume":"25","author":"Wikramaratna","year":"2020","journal-title":"Eurosurveillance"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"e200034","DOI":"10.1148\/ryct.2020200034","article-title":"Imaging profile of the COVID-19 infection: Radiologic findings and literature review","volume":"2","author":"Ng","year":"2020","journal-title":"Radiol. Cardiothorac. Imaging"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1016\/S0140-6736(20)30183-5","article-title":"Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China","volume":"395","author":"Huang","year":"2020","journal-title":"Lancet"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"e32","DOI":"10.1148\/radiol.2020200642","article-title":"Correlation of Chest CT and RT-PCR Testing for Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 Cases","volume":"296","author":"Ai","year":"2020","journal-title":"Radiology"},{"key":"ref_14","unstructured":"American College of Radiology (2021, June 01). ACR Recommendations for the Use of Chest Radiography and Computed Tomography (CT) for Suspected COVID-19 Infection. Available online: https:\/\/www.acr.org\/Advocacy-and-Economics\/ACR-Position-Statements\/Recommendations-for-Chest-Radiography-and-CT-for-Suspected-COVID19-Infection."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"e63","DOI":"10.1148\/radiol.2020203173","article-title":"Use of Chest Imaging in the Diagnosis and Management of COVID-19: A WHO Rapid Advice Guide","volume":"298","author":"Akl","year":"2020","journal-title":"Radiology"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1612","DOI":"10.1001\/jama.2020.4326","article-title":"Characteristics and outcomes of 21 critically ill patients with COVID-19 in Washington State","volume":"323","author":"Arentz","year":"2020","journal-title":"JAMA"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"e209004","DOI":"10.1148\/ryct.2020209004","article-title":"Extension of Coronavirus Disease 2019 on Chest CT and Implications for Chest Radiographic Interpretation","volume":"2","author":"Choi","year":"2020","journal-title":"Radiol. Cardiothorac. Imaging"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"e72","DOI":"10.1148\/radiol.2020201160","article-title":"Frequency and distribution of chest radiographic findings in Patients Positive for COVID-19","volume":"296","author":"Wong","year":"2020","journal-title":"Radiology"},{"key":"ref_19","unstructured":"Heneghan, C., Pluddeman, A., and Mahtani, K. (2021, June 01). Differentiating Viral from Bacterial Pneumonia. Available online: https:\/\/www.cebm.net\/covid-19\/differentiating-viral-from-bacterial-pneumonia\/."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"e7","DOI":"10.1016\/j.jinf.2020.03.007","article-title":"Clinical and CT imaging features of the COVID-19 pneumonia: Focus on pregnant women and children","volume":"80","author":"Liu","year":"2020","journal-title":"J. Infect."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"609","DOI":"10.1016\/j.acra.2020.03.002","article-title":"Imaging features of coronavirus disease 2019 (COVID-19): Evaluation on thin-section CT","volume":"27","author":"Guan","year":"2020","journal-title":"Acad. Radiol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"810","DOI":"10.1007\/s00117-013-2537-y","article-title":"Turnaround time for reporting results of radiological examinations in intensive care unit patients: An internal quality control","volume":"53","author":"Albrecht","year":"2013","journal-title":"Radiologe"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1016\/j.crad.2020.03.008","article-title":"A British Society of Thoracic Imaging statement: Considerations in designing local imaging diagnostic algorithms for the COVID-19 pandemic","volume":"75","author":"Nair","year":"2020","journal-title":"Clin. Radiol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.inffus.2021.04.008","article-title":"A critic evaluation of methods for COVID-19 automatic detection from X-ray images","volume":"76","author":"Maguolo","year":"2021","journal-title":"Inf. Fusion"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"e271","DOI":"10.1016\/S2589-7500(19)30123-2","article-title":"A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: A systematic review and meta-analysis","volume":"1","author":"Liu","year":"2019","journal-title":"Lancet Digit. Health"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"478","DOI":"10.1109\/TMI.2019.2928790","article-title":"Deep Learning of Static and Dynamic Brain Functional Networks for Early MCI Detection","volume":"39","author":"Kam","year":"2020","journal-title":"IEEE Trans. Med. Imag."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"866","DOI":"10.1109\/TMI.2019.2936500","article-title":"Deeply-Supervised Networks with Threshold Loss for Cancer Detection in Automated Breast Ultrasound","volume":"39","author":"Wang","year":"2019","journal-title":"IEEE Trans. Med. Imag."},{"key":"ref_28","unstructured":"Gordaliza, P.M., Vaquero, J.J., Sharpe, S., Gleeson, F., and Munoz-Barrutia, A. (2019). A Multi-Task Self-Normalizing 3D-CNN to Infer Tuberculosis Radiological Manifestations. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet Classification with Deep Convolutional Neural Networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_30","unstructured":"Rajpurkar, P., Irvin, J., Zhu, K., Yang, B., Mehta, H., Duan, T., Ding, D., Bagul, A., Langlotz, C., and Shpanskaya, K. (2017). CheXNet: Radiologist-level pneumonia detection on chest X-rays with deep learning. arXiv."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1148\/radiol.2017162326","article-title":"Deep learning at chest radiography: Automated classification of pulmonary tuberculosis by using convolutional neural networks","volume":"284","author":"Lakhani","year":"2017","journal-title":"Radiology"},{"key":"ref_32","unstructured":"Rajpurkar, P., Irvin, J., Bagul, A., Ding, D., Duan, T., Mehta, H., Yang, B., Zhu, K., Laird, D., and Ball, R.L. (2017). MURA: Large dataset for abnormality detection in musculoskeletal radiographs. arXiv."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1148\/radiol.2019181960","article-title":"Deep Learning\u2013based Image Conversion of CT Reconstruction Kernels Improves Radiomics Reproducibility for Pulmonary Nodules or Masses","volume":"292","author":"Choe","year":"2019","journal-title":"Radiology"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Nahid, A.A., Sikder, N., Bairagi, A.K., Razzaque, M., Masud, M., Z Kouzani, A., and Mahmud, M. (2020). A Novel Method to Identify Pneumonia through Analyzing Chest Radiographs Employing a Multichannel Convolutional Neural Network. Sensors, 20.","DOI":"10.3390\/s20123482"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2349","DOI":"10.1007\/s00345-019-03059-0","article-title":"Application of artificial neural networks for automated analysis of cystoscopic images: A review of the current status and future prospects","volume":"38","author":"Negassi","year":"2020","journal-title":"World J. Urol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"741","DOI":"10.1038\/s41551-018-0301-3","article-title":"Development and validation of a deep-learning algorithm for the detection of polyps during colonoscopy","volume":"2","author":"Wang","year":"2018","journal-title":"Nat. Biomed. Eng."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Chouhan, V., Singh, S.K., Khamparia, A., Gupta, D., Tiwari, P., Moreira, C., Dama\u0161evi\u010dius, R., and de Albuquerque, V.H.C. (2020). A Novel Transfer Learning Based Approach for Pneumonia Detection in Chest X-ray Images. Appl. Sci., 10.","DOI":"10.3390\/app10020559"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"ImageNet Large Scale Visual Recognition Challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Gu, X., Pan, L., Liang, H., and Yang, R. (2018, January 16\u201318). Classification of bacterial and viral childhood pneumonia using deep learning in chest radiography. Proceedings of the 3rd International Conference on Multimedia and Image Processing (ICMIP), Guiyang, China.","DOI":"10.1145\/3195588.3195597"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-Net: Convolutional Networks for Biomedical Image Segmentation. Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_41","unstructured":"Dosovitskiy, A., Springenberg, J.T., Riedmiller, M., and Brox, T. (2014, January 8\u201313). Discriminative Unsupervised Feature Learning with Convolutional Neural Networks. Proceedings of the 27th International Conference on Neural Information Processing Systems (NIPS), Montreal, QC, Canada."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Ho, T.K.K., and Gwak, J. (2019). Multiple feature integration for classification of thoracic disease in chest radiography. Appl. Sci., 9.","DOI":"10.3390\/app9194130"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., and Summers, R.M. (2017, January 21\u201326). ChestX-ray8: Hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.369"},{"key":"ref_47","unstructured":"Simonyan, K., and Zisserman, A. (2015, January 7\u20139). Very deep convolutional networks for large-scale image recognition. Proceedings of the 3rd International Conference on Learning Representations (ICLR), San Diego, CA, USA."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"6096","DOI":"10.1007\/s00330-021-07715-1","article-title":"A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19)","volume":"31","author":"Wang","year":"2021","journal-title":"Eur. Radiol."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Narin, A., Kaya, C., and Pamuk, Z. (2020). Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks. arXiv.","DOI":"10.1007\/s10044-021-00984-y"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V., and Alemi, A.A. (2017, January 4\u20139). Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning. Proceedings of the 31st AAAI Conference on Artificial Intelligence, San Francisco, CA, USA.","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016, January 27\u201330). Rethinking the Inception Architecture for Computer Vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"19549","DOI":"10.1038\/s41598-020-76550-z","article-title":"COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images","volume":"10","author":"Wang","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"132665","DOI":"10.1109\/ACCESS.2020.3010287","article-title":"Can AI Help in Screening Viral and COVID-19 Pneumonia?","volume":"8","author":"Chowdhury","year":"2020","journal-title":"IEEE Access"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"100405","DOI":"10.1016\/j.imu.2020.100405","article-title":"COVID faster R\u2013CNN: A novel framework to diagnose novel coronavirus disease (COVID-19) in X-ray images","volume":"20","author":"Shibly","year":"2020","journal-title":"Inform. Med. Unlocked"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1391","DOI":"10.1016\/j.bbe.2020.08.008","article-title":"A deep learning approach to detect COVID-19 coronavirus with X-Ray images","volume":"40","author":"Jain","year":"2020","journal-title":"Biocybern. Biomed. Eng."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"17532","DOI":"10.1038\/s41598-020-74539-2","article-title":"Automatic classification between COVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy on chest X-ray image: Combination of data augmentation methods in a small dataset","volume":"10","author":"Nishio","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.C. (2018, January 18\u201323). Mobilenetv2: Inverted residuals and linear bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_59","unstructured":"Tan, M., and Le, Q.V. (2019, January 9\u201315). EfficientNet: Rethinking model scaling for convolutional neural networks. Proceedings of the 36th International Conference on Machine Learning (ICML), Long Beach, CA, USA."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1007\/s13246-020-00865-4","article-title":"Covid-19: Automatic detection from X-ray images utilizing transfer learning with convolutional neural networks","volume":"43","author":"Apostolopoulos","year":"2020","journal-title":"Phys. Eng. Sci. Med."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 21\u201326). Xception: Deep learning with depthwise separable convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"15364","DOI":"10.1038\/s41598-020-71294-2","article-title":"COVID-19 image classification using deep features and fractional-order marine predators algorithm","volume":"10","author":"Sahlol","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"2921","DOI":"10.1109\/JSEN.2020.3028494","article-title":"A Movement Detection System Using Continuous-Wave Doppler Radar Sensor and Convolutional Neural Network to Detect Cough and Other Gestures","volume":"21","author":"Chuma","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_64","unstructured":"Italian Society of Medical and Interventional Radiology (2021, June 01). COVID-19 Database. Available online: https:\/\/www.sirm.org\/en\/category\/articles\/covid-19-database\/."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Cohen, J.P., Morrison, P., Dao, L., Roth, K., Duong, T.Q., and Ghassemi, M. (2020). COVID-19 Image Data Collection: Prospective Predictions Are the Future. arXiv.","DOI":"10.59275\/j.melba.2020-48g7"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1122","DOI":"10.1016\/j.cell.2018.02.010","article-title":"Identifying medical diagnoses and treatable diseases by image-based deep learning","volume":"172","author":"Kermany","year":"2018","journal-title":"Cell"},{"key":"ref_67","unstructured":"Perez, L., and Wang, J. (2017). The effectiveness of data augmentation in image classification using deep learning. arXiv."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Wong, S.C., Gatt, A., Stamatescu, V., and McDonnell, M.D. (December, January 30). Understanding data augmentation for classification: When to warp?. Proceedings of the 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA), Gold Coast, QLD, Australia.","DOI":"10.1109\/DICTA.2016.7797091"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"312","DOI":"10.1016\/j.icte.2020.04.010","article-title":"The effect of batch size on the generalizability of the convolutional neural networks on a histopathology dataset","volume":"6","author":"Kandel","year":"2020","journal-title":"ICT Express"},{"key":"ref_70","unstructured":"Lin, M., Chen, Q., and Yan, S. (2014, January 14\u201316). Network in network. Proceedings of the International Conference on Learning Representations (ICLR), Banff, AB, Canada."},{"key":"ref_71","unstructured":"Chollet, F. (2018). Deep Learning with Python, Manning Publications."},{"key":"ref_72","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_73","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 6\u201311). Batch normalization: Accelerating deep network training by reducing internal covariate shift. Proceedings of the 32nd International Conference on Machine Learning (ICML), Lille, France."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1007\/s10916-018-0932-7","article-title":"Classification of Alzheimer\u2019s disease based on eight-layer convolutional neural network with leaky rectified linear unit and max pooling","volume":"42","author":"Wang","year":"2018","journal-title":"J. Med. Syst."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"826","DOI":"10.1021\/ci00027a006","article-title":"Neural network studies. 1. Comparison of overfitting and overtraining","volume":"35","author":"Tetko","year":"1995","journal-title":"J. Chem. Inf. Comput. Sci."},{"key":"ref_76","unstructured":"Bjorck, J., Gomes, C., Selman, B., and Weinberger, K.Q. (2018, January 3\u20138). Understanding batch normalization. Proceedings of the 32nd Conference on Neural Information Processing Systems (NIPS), Montreal, QC, Canada."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.geoderma.2019.06.016","article-title":"Convolutional neural network for simultaneous prediction of several soil properties using visible\/near-infrared, mid-infrared, and their combined spectra","volume":"352","author":"Ng","year":"2019","journal-title":"Geoderma"},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Zoph, B., Vasudevan, V., Shlens, J., and Le, Q.V. (2018, January 18\u201323). Learning transferable architectures for scalable image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00907"},{"key":"ref_79","unstructured":"Real, E., Aggarwal, A., Huang, Y., and Le, Q.V. (February, January 27). Regularized evolution for image classifier architecture search. Proceedings of the AAAI Conference on Artificial Intelligence, Honolulu, HI, USA."},{"key":"ref_80","unstructured":"Winther, H.B., Laser, H., Gerbel, S., Maschke, S.K., Hinrichs, J.B., Vogel-Claussen, J., Wacker, F.K., H\u00f6per, M.M., and Meyer, B.C. (2020). Dataset: COVID-19 Image Repository, Hannover Medical School."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"106912","DOI":"10.1016\/j.asoc.2020.106912","article-title":"CNN-based transfer learning\u2013BiLSTM network: A novel approach for COVID-19 infection detection","volume":"98","author":"Aslan","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"113","DOI":"10.32628\/IJSRST207614","article-title":"Image Pre-processing techniques comparison: COVID-19 detection through Chest X-Rays via Deep Learning","volume":"7","author":"Maity","year":"2020","journal-title":"Int. J. Sci. Res. Sci. Technol."},{"key":"ref_83","unstructured":"\u00d6ks\u00fcz, C., Urhan, O., and G\u00fcll\u00fc, M.K. (2020). Ensemble-CVDNet: A Deep Learning based End-to-End Classification Framework for COVID-19 Detection using Ensembles of Networks. arXiv."},{"key":"ref_84","unstructured":"Progga, N.I., Hossain, M.S., and Andersson, K. (2020, January 26\u201327). A Deep Transfer Learning Approach to Diagnose COVID-19 using X-ray Images. Proceedings of the 2020 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE), Bhubaneswar, India."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Sakib, S., Siddique, M.A.B., Rahman Khan, M.M., Yasmin, N., Aziz, A., Chowdhury, M., and Tasawar, I.K. (2020). Detection of COVID-19 Disease from Chest X-Ray Images: A Deep Transfer Learning Framework. medRxiv.","DOI":"10.1101\/2020.11.08.20227819"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/17\/5702\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:50:48Z","timestamp":1760165448000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/17\/5702"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,24]]},"references-count":85,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2021,9]]}},"alternative-id":["s21175702"],"URL":"https:\/\/doi.org\/10.3390\/s21175702","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,24]]}}}