{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T20:58:46Z","timestamp":1770843526981,"version":"3.50.1"},"reference-count":31,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,12,16]],"date-time":"2024-12-16T00:00:00Z","timestamp":1734307200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Big Data"],"DOI":"10.3389\/fdata.2024.1489020","type":"journal-article","created":{"date-parts":[[2024,12,16]],"date-time":"2024-12-16T12:22:12Z","timestamp":1734351732000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Leveraging compact convolutional transformers for enhanced COVID-19 detection in chest X-rays: a grad-CAM visualization approach"],"prefix":"10.3389","volume":"7","author":[{"given":"Aravinda C.","family":"V","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sudeepa K.","family":"B","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"S.","family":"Pradeep","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"P.","family":"Suraksha","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meng","family":"Lin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2024,12,16]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"E32","DOI":"10.1148\/radiol.2020200642","article-title":"Correlation of chest CT and RT-pcr testing in coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases","volume":"296","author":"Ai","year":"2020","journal-title":"Radiology"},{"key":"B2","doi-asserted-by":"publisher","first-page":"5913905","DOI":"10.1155\/2022\/5913905","article-title":"Segmentation and classification of white blood cells using the unet","volume":"571","author":"Alharbi","year":"2022","journal-title":"Contrast Media Mol. Imag."},{"key":"B3","doi-asserted-by":"publisher","first-page":"9171343","DOI":"10.1155\/2022\/9171343","article-title":"Computational models-based detection of peripheral malarial parasites in blood smears","volume":"2022","author":"Alharbi","year":"","journal-title":"Contrast Media Mol. Imag"},{"key":"B4","doi-asserted-by":"publisher","first-page":"3922763","DOI":"10.1155\/2022\/3922763","article-title":"Detection of peripheral malarial parasites in blood smears using deep learning models","volume":"2022","author":"Alharbi","year":"","journal-title":"Comput. Intell. Neurosci"},{"key":"B5","doi-asserted-by":"publisher","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":"B6","doi-asserted-by":"publisher","first-page":"20170387","DOI":"10.1098\/rsif.2017.0387","article-title":"Opportunities and obstacles for deep learning in biology and medicine","volume":"15","author":"Ching","year":"2018","journal-title":"J. R. Soc. Interface"},{"key":"B7","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1148\/radiol.2019181960","article-title":"Deep learning-based image conversion of CT reconstruction kernels improves radiomics reproducibility for pulmonary nodules or masses","volume":"292","author":"Choe","year":"2019","journal-title":"Radiology"},{"key":"B8","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/j.bsheal.2020.05.002","article-title":"Charting the challenges behind the testing of COVID-19 in developing countries: Nepal as a case study","volume":"2","author":"Giri","year":"2020","journal-title":"Biosaf Health"},{"key":"B9","article-title":"Lobar distribution of COVID-19 pneumonia based on chest computed tomography findings: a retrospective study","author":"Haseli","year":"2020","journal-title":"Arch. Acad. Emerg. Med"},{"key":"B10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90","article-title":"\u201cDeep residual learning for image recognition,\u201d","author":"He","year":"2016","journal-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition"},{"key":"B11","article-title":"Covidx-net: a framework of deep learning classifiers to diagnose covid-19 in x-ray images","author":"Hemdan","year":"2020","journal-title":"arXiv preprint arXiv:2003.11055"},{"key":"B12","doi-asserted-by":"publisher","first-page":"100412","DOI":"10.1016\/j.imu.2020.100412","article-title":"A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images","volume":"20","author":"Islam","year":"2020","journal-title":"Inform. Med. Unlocked"},{"key":"B13","doi-asserted-by":"publisher","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":"B14","doi-asserted-by":"publisher","first-page":"e200028","DOI":"10.1148\/ryct.2020200028","article-title":"Chest imaging appearance of COVID-19 infection","volume":"2","author":"Kong","year":"2020","journal-title":"Radiol. Cardiothorac. Imag."},{"key":"B15","doi-asserted-by":"publisher","first-page":"E65","DOI":"10.1148\/radiol.2020200905","article-title":"Artificial intelligence distinguishes COVID-19 from community-acquired pneumonia on chest CT","volume":"296","author":"Li","year":"","journal-title":"Radiology"},{"key":"B16","doi-asserted-by":"publisher","DOI":"10.1101\/2020.04.28.20082784","article-title":"COVID-19 versus non-COVID-19 pneumonia: a retrospective cohort study in Chengdu, China","author":"Li","year":"","journal-title":"medRxiv 2020\u201304"},{"key":"B17","article-title":"A new modified deep convolutional neural network for detecting COVID-19 from X-ray images","author":"Mohammad","year":"2020","journal-title":"arXiv preprint arXiv:2004.08052"},{"key":"B18","doi-asserted-by":"publisher","first-page":"1207","DOI":"10.1007\/s10044-021-00984-y","article-title":"Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks","volume":"24","author":"Narin","year":"2021","journal-title":"Pattern Anal. Applic."},{"key":"B19","doi-asserted-by":"publisher","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. Imag."},{"key":"B20","doi-asserted-by":"publisher","first-page":"103792","DOI":"10.1016\/j.compbiomed.2020.103792","article-title":"Automated detection of COVID-19 cases using deep neural networks with X-ray images","volume":"121","author":"Ozturk","year":"2020","journal-title":"Comput. Biol. Med."},{"key":"B21","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1007\/s10489-020-01831-z","article-title":"Detection of COVID-19 using CXR and CT images using Transfer Learning and Haralick features","volume":"51","author":"Perumal","year":"2020","journal-title":"Appl. Intell"},{"key":"B22","first-page":"2369","article-title":"CNN analysis for the detection of SARS-CoV-2 in human body","volume":"29","author":"Saha","year":"2020","journal-title":"Int. J. Adv. Sci. Technol"},{"key":"B23","author":"Selvaraju","year":"2016"},{"key":"B24","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/RBME.2020.2987975","article-title":"Review of artificial intelligence techniques in imaging data acquisition, segmentation, and diagnosis for COVID-19","volume":"14","author":"Shi","year":"2021","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"B25","first-page":"1","article-title":"\u201cVery deep convolutional networks for large-scale image recognition,\u201d","author":"Simonyan","year":"2015","journal-title":"3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings"},{"key":"B26","doi-asserted-by":"publisher","first-page":"75872","DOI":"10.1109\/ACCESS.2022.3191429","article-title":"Dknet: deep kuzushiji characters recognition network","volume":"10","author":"Singh","year":"2022","journal-title":"IEEE Access"},{"key":"B27","article-title":"SARS-CoV-2 CT-scan dataset: a large dataset of real patients CT scans for SARS-CoV-2 identification","author":"Soares","year":"2020","journal-title":"MedRxiv 2020\u201304"},{"key":"B28","doi-asserted-by":"publisher","first-page":"2775","DOI":"10.1109\/TCBB.2021.3065361","article-title":"Deep learning enables accurate diagnosis of novel coronavirus (COVID-19) with CT images","volume":"18","author":"Song","year":"2021","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform"},{"key":"B29","doi-asserted-by":"publisher","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":"B30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1183\/13993003.00775-2020","article-title":"A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis","volume":"56","author":"Wang","year":"2020","journal-title":"Eur. Respir. J"},{"key":"B31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.369","article-title":"\u201cChestX-ray8: hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases,\u201d","author":"Wang","year":"2017","journal-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition"}],"container-title":["Frontiers in Big Data"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fdata.2024.1489020\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,16]],"date-time":"2024-12-16T12:22:15Z","timestamp":1734351735000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fdata.2024.1489020\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,16]]},"references-count":31,"alternative-id":["10.3389\/fdata.2024.1489020"],"URL":"https:\/\/doi.org\/10.3389\/fdata.2024.1489020","relation":{},"ISSN":["2624-909X"],"issn-type":[{"value":"2624-909X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,16]]},"article-number":"1489020"}}