{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T21:58:23Z","timestamp":1789682303096,"version":"4.0.1"},"reference-count":32,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIRx Med"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n SARS-CoV-2, the causative agent of COVID-19, remains a global health concern due to its high transmissibility and evolving variants. Although vaccination efforts and therapeutic advancements have mitigated disease severity, emerging mutations continue to challenge diagnostics and containment strategies. As of mid-February 2025, global test positivity has risen to 11%, marking the highest level in over 6 months, despite widespread immunization efforts. Newer variants demonstrate enhanced host cell binding, increasing both infectivity and diagnostic complexity.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n This study aimed to evaluate the effectiveness of deep transfer learning in delivering a rapid, accurate, and mutation-resilient COVID-19 diagnosis from medical imaging, with a focus on scalability and accessibility.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n An automated detection system was developed using state-of-the-art convolutional neural networks, including VGG16 (Visual Geometry Group network-16 layers), ResNet50 (residual network-50 layers), ConvNeXtTiny (convolutional next-tiny), MobileNet (mobile network), NASNetMobile (neural architecture search network-mobile version), and DenseNet121 (densely connected convolutional network-121 layers), to detect COVID-19 from chest X-ray and computed tomography (CT) images.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n Among all the models evaluated, DenseNet121 emerged as the best-performing architecture for COVID-19 diagnosis using X-ray and CT images. It achieved an impressive accuracy of 98%, with a precision of 96.9%, a recall of 98.9%, an\n F<\/jats:italic>\n 1<\/jats:sub>\n -score of 97.9%, and an area under the curve score of 99.8%, indicating a high degree of consistency and reliability in detecting both positive and negative cases. The confusion matrix showed minimal false positives and false negatives, underscoring the model\u2019s robustness in real-world diagnostic scenarios. Given its performance, DenseNet121 is a strong candidate for deployment in clinical settings and serves as a benchmark for future improvements in artificial intelligence\u2013assisted diagnostic tools.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n The study results underscore the potential of artificial intelligence\u2013powered diagnostics in supporting early detection and global pandemic response. With careful optimization, deep learning models can address critical gaps in testing, particularly in settings constrained by limited resources or emerging variants.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/75015","type":"journal-article","created":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T14:55:07Z","timestamp":1756479307000},"page":"e75015-e75015","source":"Crossref","is-referenced-by-count":11,"title":["COVID-19 Pneumonia Diagnosis Using Medical Images: Deep Learning\u2013Based Transfer Learning Approach"],"prefix":"10.2196","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-2452-8155","authenticated-orcid":false,"given":"Anjali","family":"Dharmik","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2025,9,26]]},"reference":[{"key":"R1","unstructured":"Pneumonia of unknown cause \u2013 China. World Health Organization. 2020. URL: https:\/\/www.who.int\/emergencies\/disease-outbreak-news\/item\/2020-DON229 [Accessed 12-09-2025]"},{"key":"R2","unstructured":"Coronavirus disease (COVID-19) - Overview. World Health Organization. URL: https:\/\/www.who.int\/health-topics\/coronavirus#tab=tab_1 [Accessed 12-09-2025]"},{"key":"R3","unstructured":"COVID-19: new variants in 2025. Ada Health. 2025. URL: https:\/\/ada.com\/covid\/what-strain-of-covid-is-going-around\/ [Accessed 12-09-2025]"},{"key":"R4","unstructured":"Weekly epidemiological update on COVID-19 - 25 August 2023. World Health Organization. 2023. URL: https:\/\/www.who.int\/publications\/m\/item\/weekly-epidemiological-update-on-covid-19---30-august-2023 [Accessed 12-09-2025]"},{"key":"R5","unstructured":"COVID-19 Public Health Emergency of International Concern (PHEIC) Global research and innovation forum. World Health Organization. 2020. URL: https:\/\/www.who.int\/publications\/m\/item\/covid-19-public-health-emergency-of-international-concern-%28pheic%29-global-research-and-innovation-forum [Accessed 12-09-2025]"},{"key":"R6","unstructured":"COVID-19 - global situation. World Health Organization. 2025. URL: https:\/\/www.who.int\/emergencies\/disease-outbreak-news\/item\/2025-DON572 [Accessed 12-09-2025]"},{"key":"R7","unstructured":"Katella K . 3 things to know about XEC, the dominant COVID strain. Yale Medicine. 2024. URL: https:\/\/www.yalemedicine.org\/news\/3-things-to-know-about-xec-the-latest-covid-strain [Accessed 12-09-2025]"},{"key":"R8","unstructured":"Surveillance and data analytics. Centers for Disease Control and Prevention. 2025. URL: https:\/\/covid.cdc.gov\/covid-data-tracker [Accessed 12-09-2025]"},{"key":"R9","unstructured":"WHO\u2019s 2025 updates on COVID-19 variants: focus on XEC, testing, and recovery. ASSURE. 2025. URL: https:\/\/assure-test.com\/2025\/02\/05\/whos-2025-updates-on-covid-19-variants-focus-on-xec-testing-and-recovery\/ [Accessed 12-09-2025]"},{"key":"R10","unstructured":"Tracking SARS-CoV-2 variants. World Health Organization. 2025. URL: https:\/\/www.who.int\/activities\/tracking-sars-cov-2-variants [Accessed 12-09-2025]"},{"key":"R11","unstructured":"Coronavirus disease (COVID-19) - Symptoms. World Health Organization. URL: https:\/\/www.who.int\/health-topics\/coronavirus#tab=tab_3 [Accessed 12-09-2025]"},{"key":"R12","unstructured":"COVID-19 symptoms: Omicron vs. Delta. Ada Health. 2025. URL: https:\/\/ada.com\/covid\/covid-19-omicron-vs-delta-symptoms\/ [Accessed 12-09-2025]"},{"issue":"1","key":"R13","doi-asserted-by":"publisher","DOI":"10.1007\/s42979-020-00401-x","article-title":"Scalable telehealth services to combat novel coronavirus (COVID-19) pandemic","volume":"2","author":"Ullah","journal-title":"SN Comput Sci"},{"issue":"1","key":"R14","doi-asserted-by":"publisher","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","journal-title":"Sci Rep"},{"issue":"11","key":"R15","doi-asserted-by":"publisher","DOI":"10.3390\/healthcare11111561","article-title":"Detection of pneumonia from chest x-ray images utilizing MobileNet model","volume":"11","author":"Reshan","journal-title":"Healthcare (Basel)"},{"issue":"5","key":"R16","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics12051280","article-title":"Pneumonia classification from x-ray images with Inception-V3 and Convolutional Neural Network","volume":"12","author":"Mujahid","journal-title":"Diagnostics (Basel)"},{"issue":"4","key":"R17","doi-asserted-by":"publisher","DOI":"10.2196\/27468","article-title":"Deep convolutional neural network-based computer-aided detection system for COVID-19 using multiple lung scans: design and implementation study","volume":"23","author":"Ghaderzadeh","journal-title":"J Med Internet Res"},{"issue":"11","key":"R18","doi-asserted-by":"publisher","DOI":"10.3390\/v14112324","article-title":"An update on detection technologies for SARS-CoV-2 variants of concern","volume":"14","author":"Jiang","journal-title":"Viruses"},{"issue":"11","key":"R19","doi-asserted-by":"publisher","DOI":"10.2196\/39536","article-title":"Developing an artificial intelligence model for reading chest x-rays: protocol for a prospective validation study","volume":"11","author":"Mir\u00f3 Catalina","journal-title":"JMIR Res Protoc"},{"issue":"1","key":"R20","doi-asserted-by":"publisher","DOI":"10.1080\/22221751.2024.2402880","article-title":"Recurrent SARS-CoV-2 spike mutations confer growth advantages to select JN.1 sublineages","volume":"13","author":"Wang","journal-title":"Emerg Microbes Infect"},{"key":"R21","unstructured":"Scott Mader K . The Lung Image Database Consortium image collection (LIDC-IDRI). IEEE DataPort. 2021. URL: https:\/\/ieee-dataport.org\/documents\/lung-image-database-consortium-image-collection-lidc-idri [Accessed 12-09-2025]"},{"key":"R22","unstructured":"SIRM - Societ\u00e0 Italiana di Radiologia Medica e Interventistica. URL: https:\/\/sirm.org [Accessed 12-09-2025]"},{"key":"R23","unstructured":"BIMCV-COVID19, Conjuntos de datos relacionados con el curso de patolog\u00eda de COVID19 [Article in Spanish]. BIMCV. 2023. URL: https:\/\/bimcv.cipf.es\/bimcv-projects\/bimcv-covid19\/ [Accessed 12-09-2025]"},{"key":"R24","unstructured":"iCTCF: CT images and clinical features for COVID-19. National Genomics Data Center - China National Center for Bioinformation. URL: https:\/\/ngdc.cncb.ac.cn\/ictcf\/ [Accessed 12-09-2025]"},{"key":"R25","unstructured":"CT images in COVID-19. The Cancer Imaging Archive. URL: https:\/\/www.cancerimagingarchive.net\/collection\/ct-images-in-covid-19\/ [Accessed 12-09-2025]"},{"key":"R26","unstructured":"MIDRC-RICORD-1A. The Cancer Imaging Archive. URL: https:\/\/www.cancerimagingarchive.net\/collection\/midrc-ricord-1a\/ [Accessed 12-09-2025]"},{"key":"R27","unstructured":"MIDRC-RICORD-1B. The Cancer Imaging Archive. URL: https:\/\/www.cancerimagingarchive.net\/collection\/midrc-ricord-1b\/ [Accessed 12-09-2025]"},{"key":"R28","unstructured":"MIDRC-RICORD-1C. The Cancer Imaging Archive. URL: https:\/\/www.cancerimagingarchive.net\/collection\/midrc-ricord-1c\/ [Accessed 12-09-2025]"},{"key":"R29","unstructured":"STOIC2021 - COVID-19 AI Challenge. STOIC2021 Grand Challenge. URL: https:\/\/stoic2021.grand-challenge.org\/ [Accessed 12-09-2025]"},{"key":"R30","unstructured":"COVID-19. Radiopaedia. URL: https:\/\/radiopaedia.org\/articles\/covid-19-4 [Accessed 12-09-2025]"},{"key":"R31","unstructured":"Datasets [Article in Russian]. Center of Diagnostics and Telemedicine. URL: https:\/\/mosmed.ai\/en\/datasets\/ [Accessed 12-09-2025]"},{"key":"R32","unstructured":"Dharmik A . COVID-19-APP. GitHub. 2025. URL: https:\/\/github.com\/AnjaliDharmik\/COVID-19-APP [Accessed 12-09-2025]"}],"container-title":["JMIRx Med"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/xmed.jmir.org\/2025\/1\/e75015","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T18:05:51Z","timestamp":1782842751000},"score":1,"resource":{"primary":{"URL":"https:\/\/xmed.jmir.org\/2025\/1\/e75015"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,26]]},"references-count":32,"URL":"https:\/\/doi.org\/10.2196\/75015","relation":{},"ISSN":["2563-6316"],"issn-type":[{"value":"2563-6316","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,26]]},"article-number":"v6i1e75015"}}