{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T16:40:39Z","timestamp":1783788039569,"version":"3.55.0"},"reference-count":38,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,10]],"date-time":"2023-01-10T00:00:00Z","timestamp":1673308800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Bolsa de Investiga\u00e7\u00e3o (BI)","award":["PRODERAM20-16.2.2-FEADER-1810"],"award-info":[{"award-number":["PRODERAM20-16.2.2-FEADER-1810"]}]},{"name":"Bolsa de Investiga\u00e7\u00e3o (BI)","award":["Projeto\u2014UIDB\/50009\/2020"],"award-info":[{"award-number":["Projeto\u2014UIDB\/50009\/2020"]}]},{"name":"Bolsa de Investiga\u00e7\u00e3o (BI)","award":["M1420-09-5369-FSE-000002"],"award-info":[{"award-number":["M1420-09-5369-FSE-000002"]}]},{"name":"LARSyS","award":["PRODERAM20-16.2.2-FEADER-1810"],"award-info":[{"award-number":["PRODERAM20-16.2.2-FEADER-1810"]}]},{"name":"LARSyS","award":["Projeto\u2014UIDB\/50009\/2020"],"award-info":[{"award-number":["Projeto\u2014UIDB\/50009\/2020"]}]},{"name":"LARSyS","award":["M1420-09-5369-FSE-000002"],"award-info":[{"award-number":["M1420-09-5369-FSE-000002"]}]},{"name":"RDITI\u2014Ag\u00eancia Regional para o Desenvolvimento da Investiga\u00e7\u00e3o, Tecnologia e Inova\u00e7\u00e3o","award":["PRODERAM20-16.2.2-FEADER-1810"],"award-info":[{"award-number":["PRODERAM20-16.2.2-FEADER-1810"]}]},{"name":"RDITI\u2014Ag\u00eancia Regional para o Desenvolvimento da Investiga\u00e7\u00e3o, Tecnologia e Inova\u00e7\u00e3o","award":["Projeto\u2014UIDB\/50009\/2020"],"award-info":[{"award-number":["Projeto\u2014UIDB\/50009\/2020"]}]},{"name":"RDITI\u2014Ag\u00eancia Regional para o Desenvolvimento da Investiga\u00e7\u00e3o, Tecnologia e Inova\u00e7\u00e3o","award":["M1420-09-5369-FSE-000002"],"award-info":[{"award-number":["M1420-09-5369-FSE-000002"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJERPH"],"abstract":"<jats:p>The number of coronavirus disease (COVID-19) cases is constantly rising as the pandemic continues, with new variants constantly emerging. Therefore, to prevent the virus from spreading, coronavirus cases must be diagnosed as soon as possible. The COVID-19 pandemic has had a devastating impact on people\u2019s health and the economy worldwide. For COVID-19 detection, reverse transcription-polymerase chain reaction testing is the benchmark. However, this test takes a long time and necessitates a lot of laboratory resources. A new trend is emerging to address these limitations regarding the use of machine learning and deep learning techniques for automatic analysis, as these can attain high diagnosis results, especially by using medical imaging techniques. However, a key question arises whether a chest computed tomography scan or chest X-ray can be used for COVID-19 detection. A total of 17,599 images were examined in this work to develop the models used to classify the occurrence of COVID-19 infection, while four different classifiers were studied. These are the convolutional neural network (proposed architecture (named, SCovNet) and Resnet18), support vector machine, and logistic regression. Out of all four models, the proposed SCoVNet architecture reached the best performance with an accuracy of almost 99% and 98% on chest computed tomography scan images and chest X-ray images, respectively.<\/jats:p>","DOI":"10.3390\/ijerph20021268","type":"journal-article","created":{"date-parts":[[2023,1,11]],"date-time":"2023-01-11T01:33:12Z","timestamp":1673400792000},"page":"1268","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Novel Comparative Study for the Detection of COVID-19 Using CT Scan and Chest X-ray Images"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2000-5557","authenticated-orcid":false,"given":"Ahatsham","family":"Hayat","sequence":"first","affiliation":[{"name":"University of Madeira, 9000-082 Funchal, Portugal"},{"name":"Interactive Technologies Institute (ITI\/LARSyS and ARDITI), 9020-105 Funchal, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3348-262X","authenticated-orcid":false,"given":"Preety","family":"Baglat","sequence":"additional","affiliation":[{"name":"University of Madeira, 9000-082 Funchal, Portugal"},{"name":"Interactive Technologies Institute (ITI\/LARSyS and ARDITI), 9020-105 Funchal, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5107-3248","authenticated-orcid":false,"given":"F\u00e1bio","family":"Mendon\u00e7a","sequence":"additional","affiliation":[{"name":"University of Madeira, 9000-082 Funchal, Portugal"},{"name":"Interactive Technologies Institute (ITI\/LARSyS and ARDITI), 9020-105 Funchal, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7677-0971","authenticated-orcid":false,"given":"Sheikh Shanawaz","family":"Mostafa","sequence":"additional","affiliation":[{"name":"Interactive Technologies Institute (ITI\/LARSyS and ARDITI), 9020-105 Funchal, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7334-3993","authenticated-orcid":false,"given":"Fernando","family":"Morgado-Dias","sequence":"additional","affiliation":[{"name":"University of Madeira, 9000-082 Funchal, Portugal"},{"name":"Interactive Technologies Institute (ITI\/LARSyS and ARDITI), 9020-105 Funchal, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,10]]},"reference":[{"key":"ref_1","unstructured":"WHO (2021, December 03). Summary of Probable SARS Cases with Onset of Illness from 1 November 2002 to 31 July 2003 (Based on Data as of 31 December 2003). Available online: https:\/\/www.who.int\/home\/search?indexCatalogue=genericsearchindex1."},{"key":"ref_2","unstructured":"WHO (2021, December 03). Middle East Respiratory Syndrome Coronavirus (MERS-CoV). Available online: https:\/\/www.who.int\/health-topics\/middle-east-respiratory-syndrome-coronavirus-mers#tab=tab_1."},{"key":"ref_3","unstructured":"WHO (2021, December 03). Naming the Coronavirus Disease (COVID-2019) and the Virus That Causes It. Available online: https:\/\/www.who.int\/emergencies\/diseases\/novel-coronavirus-2019\/technical-guidance\/naming-the-coronavirus-disease-(covid-2019)-and-thevirus-that-causes-it."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"564","DOI":"10.1016\/j.jds.2020.02.002","article-title":"The impact of the COVID-19 epidemic on the utilization of emergency dental services","volume":"15","author":"Guo","year":"2020","journal-title":"J. Dent. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1016\/j.ajem.2020.03.036","article-title":"Coronavirus disease (COVID-19): A primer for emergency physicians","volume":"44","author":"Chavez","year":"2020","journal-title":"Am. J. Emerg. Med."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"19931","DOI":"10.1007\/s11042-021-10714-5","article-title":"Novel coronavirus (COVID-19) diagnosis using computer vision and artificial intelligence techniques: A review","volume":"80","author":"Bhargava","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1689","DOI":"10.1093\/aje\/kwab093","article-title":"Diagnostic Accuracy Estimates for COVID-19 Real-Time Polymerase Chain Reaction and Lateral Flow Immunoassay Tests with Bayesian Latent-Class Models","volume":"190","author":"Kostoulas","year":"2021","journal-title":"Am. J. Epidemiol."},{"key":"ref_8","first-page":"CD013652","article-title":"Antibody tests for identification of current and past infection with SARS-CoV-2","volume":"6","author":"Deeks","year":"2020","journal-title":"Cochrane Database Syst. Rev."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1038\/s41597-021-00900-3","article-title":"COVID-CT-MD, COVID-19 computed tomography scan dataset applicable in machine learning and deep learning","volume":"8","author":"Afshar","year":"2021","journal-title":"Sci. Data"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"19196","DOI":"10.1038\/s41598-020-76282-0","article-title":"Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography","volume":"10","author":"Chen","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Kassani, S.H., Kassani, P.H., Wesolowski, M.J., Schneider, K.A., and Deters, R. (2019, January 10\u201312). Depthwise separable convolutional neural network for skin lesion classification. Proceedings of the 2019 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), Ajman, United Arab Emirates.","DOI":"10.1109\/ISSPIT47144.2019.9001790"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1007\/s11547-019-00990-5","article-title":"Artificial intelligence: A challenge for third millennium radiologist","volume":"124","author":"Grassi","year":"2019","journal-title":"Radiol. Med."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1007\/s11547-020-01135-9","article-title":"Artificial intelligence: Who is responsible for the diagnosis?","volume":"125","author":"Neri","year":"2020","journal-title":"Radiol. Med."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1186","DOI":"10.1007\/s11547-020-01277-w","article-title":"Artificial intelligence in cardiac radiology","volume":"125","author":"Muscogiuri","year":"2020","journal-title":"Radiol. Med."},{"key":"ref_15","unstructured":"Mahmud, M., Kaiser, M.S., and Hussain, A. (2020). Deep learning in mining biological data. arXiv."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2063","DOI":"10.1109\/TNNLS.2018.2790388","article-title":"Applications of deep learning and reinforcement learning to biological data","volume":"29","author":"Mahmud","year":"2018","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_17","first-page":"567","article-title":"Lung nodule detection and classification from thorax ct-scan using retinanet with transfer learning","volume":"34","author":"Harsono","year":"2022","journal-title":"J. King Saud. Univ. Comput. Inf. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"110190","DOI":"10.1016\/j.chaos.2020.110190","article-title":"A deep learning and grad-CAM based color visualization approach for fast detection of COVID-19 cases using chest X-ray and CT-Scan images","volume":"140","author":"Panwar","year":"2020","journal-title":"Chaos Solitons Fractals"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"e286","DOI":"10.1016\/S2589-7500(21)00039-X","article-title":"Prognostication of patients with COVID-19 using artificial intelligence based on chest x-rays and clinical data: A retrospective study","volume":"3","author":"Jiao","year":"2021","journal-title":"Lancet Digit. Health"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Al-Waisy, A.S., Al-Fahdawi, S., Mohammed, M.A., Abdulkareem, K.H., Mostafa, S.A., Maashi, M.S., Arif, M., and Garcia-Zapirain, B. (2020). COVID-CheXNet: Hybrid deep learning framework for identifying COVID-19 virus in chest X-rays images. Soft Comput., ahead of print.","DOI":"10.1007\/s00500-020-05424-3"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Kassani, S.H., Kassasni, P.H., Wesolowski, M.J., Schneider, K.A., and Deters, R. (2020). Automatic detection of coronavirus disease (COVID-19) in x-ray and ct images: A machine learning-based approach. arXiv.","DOI":"10.1016\/j.bbe.2021.05.013"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"6658058","DOI":"10.1155\/2021\/6658058","article-title":"COVID-19 Diagnosis Using an Enhanced Inception-ResNetV2 Deep Learning Model in CXR Images","volume":"2021","author":"Alruwaili","year":"2021","journal-title":"J. Healthc. Eng."},{"key":"ref_23","unstructured":"Hemdan, E.E.-D., Shouman, M.A., and Karar, M.E. (2020). COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray Images. arXiv."},{"key":"ref_24","first-page":"271","article-title":"COVID-19 disease diagnosis using smart deep learning techniques","volume":"24","author":"Kavitha","year":"2021","journal-title":"J. Appl. Sci. Eng."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"109944","DOI":"10.1016\/j.chaos.2020.109944","article-title":"Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet","volume":"138","author":"Panwar","year":"2020","journal-title":"Chaos Solitons Fractals"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Nguyen, D., Kay, F., Tan, J., Yan, Y., Ng, Y.S., Iyengar, P., Peshock, R., and Jiang, S. (2021). Deep learning-based COVID-19 pneumonia classification using chest CT images: Model generalizability. arXiv.","DOI":"10.3389\/frai.2021.694875"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"105581","DOI":"10.1016\/j.cmpb.2020.105581","article-title":"CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images","volume":"196","author":"Khan","year":"2020","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Bukhari, S.U., Bukhari, S.S., Syed, A., and Shah, S.S. (2020). The diagnostic evaluation of Convolutional Neural Network (CNN) for the assessment of chest X-ray of patients infected with COVID-19. medRxiv, preprint.","DOI":"10.1101\/2020.03.26.20044610"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Rehman, A., Iqbal, M.A., Xing, H., and Ahmed, I. (2021). COVID-19 Detection Empowered with Machine Learning and Deep Learning Techniques: A Systematic Review. Appl. Sci., 11.","DOI":"10.3390\/app11083414"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Gouda, W., Almurafeh, M., Humayun, M., and Jhanjhi, N.Z. (2022). Detection of COVID-19 Based on Chest X-rays Using Deep Learning. Healthcare, 10.","DOI":"10.3390\/healthcare10020343"},{"key":"ref_31","unstructured":"El-Shafai, W., and Abd El-Samie, F.E. (2020). Extensive COVID-19 X-Ray and CT Chest Images Dataset. Mendeley Data, V3."},{"key":"ref_32","first-page":"5587188","article-title":"Machine Learning-Based Model to Predict the Disease Severity and Outcome in COVID-19 Patients","volume":"2021","author":"Aljameel","year":"2021","journal-title":"Sci. Program."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"7166","DOI":"10.1038\/s41598-021-86735-9","article-title":"Machine learning is the key to diagnose COVID-19: A proof-of-concept study","volume":"11","author":"Gangloff","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1055","DOI":"10.1109\/72.788646","article-title":"Support Vector Machines for Histogram-Based Image Classification","volume":"10","author":"Chapelle","year":"2018","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_35","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 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"042","DOI":"10.1088\/1742-6596\/1748\/4\/042054","article-title":"Imbalanced Thangka image classification research based on the ResNet network","volume":"1748","author":"Zeng","year":"2021","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1388","DOI":"10.3390\/electronics9091388","article-title":"Multi-Channel Transfer Learning of Chest X-ray Images for Screening of COVID-19","volume":"9","author":"Sampa","year":"2020","journal-title":"Electronics"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1007\/s11263-019-01228-7","article-title":"Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization","volume":"128","author":"Selvaraju","year":"2016","journal-title":"Int. J. Comput. Vis."}],"container-title":["International Journal of Environmental Research and Public Health"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1660-4601\/20\/2\/1268\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:05:59Z","timestamp":1760119559000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1660-4601\/20\/2\/1268"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,10]]},"references-count":38,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["ijerph20021268"],"URL":"https:\/\/doi.org\/10.3390\/ijerph20021268","relation":{},"ISSN":["1660-4601"],"issn-type":[{"value":"1660-4601","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,10]]}}}