{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T04:56:12Z","timestamp":1791608172016,"version":"4.3.4"},"reference-count":175,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,6,16]],"date-time":"2022-06-16T00:00:00Z","timestamp":1655337600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background: The previous COVID-19 lung diagnosis system lacks both scientific validation and the role of explainable artificial intelligence (AI) for understanding lesion localization. This study presents a cloud-based explainable AI, the \u201cCOVLIAS 2.0-cXAI\u201d system using four kinds of class activation maps (CAM) models. Methodology: Our cohort consisted of ~6000 CT slices from two sources (Croatia, 80 COVID-19 patients and Italy, 15 control patients). COVLIAS 2.0-cXAI design consisted of three stages: (i) automated lung segmentation using hybrid deep learning ResNet-UNet model by automatic adjustment of Hounsfield units, hyperparameter optimization, and parallel and distributed training, (ii) classification using three kinds of DenseNet (DN) models (DN-121, DN-169, DN-201), and (iii) validation using four kinds of CAM visualization techniques: gradient-weighted class activation mapping (Grad-CAM), Grad-CAM++, score-weighted CAM (Score-CAM), and FasterScore-CAM. The COVLIAS 2.0-cXAI was validated by three trained senior radiologists for its stability and reliability. The Friedman test was also performed on the scores of the three radiologists. Results: The ResNet-UNet segmentation model resulted in dice similarity of 0.96, Jaccard index of 0.93, a correlation coefficient of 0.99, with a figure-of-merit of 95.99%, while the classifier accuracies for the three DN nets (DN-121, DN-169, and DN-201) were 98%, 98%, and 99% with a loss of ~0.003, ~0.0025, and ~0.002 using 50 epochs, respectively. The mean AUC for all three DN models was 0.99 (p &lt; 0.0001). The COVLIAS 2.0-cXAI showed 80% scans for mean alignment index (MAI) between heatmaps and gold standard, a score of four out of five, establishing the system for clinical settings. Conclusions: The COVLIAS 2.0-cXAI successfully showed a cloud-based explainable AI system for lesion localization in lung CT scans.<\/jats:p>","DOI":"10.3390\/diagnostics12061482","type":"journal-article","created":{"date-parts":[[2022,6,17]],"date-time":"2022-06-17T01:48:12Z","timestamp":1655430492000},"page":"1482","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":56,"title":["COVLIAS 2.0-cXAI: Cloud-Based Explainable Deep Learning System for COVID-19 Lesion Localization in Computed Tomography Scans"],"prefix":"10.3390","volume":"12","author":[{"given":"Jasjit","family":"Suri","sequence":"first","affiliation":[{"name":"Stroke Diagnostic and Monitoring Division, AtheroPoint\u2122, Roseville, CA 95661, USA"},{"name":"Advanced Knowledge Engineering Centre, GBTI, Roseville, CA 95661, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2360-3886","authenticated-orcid":false,"given":"Sushant","family":"Agarwal","sequence":"additional","affiliation":[{"name":"Advanced Knowledge Engineering Centre, GBTI, Roseville, CA 95661, USA"},{"name":"Department of Computer Science Engineering, PSIT, Kanpur 209305, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0005-5242","authenticated-orcid":false,"given":"Gian","family":"Chabert","sequence":"additional","affiliation":[{"name":"Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09123 Cagliari, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alessandro","family":"Carriero","sequence":"additional","affiliation":[{"name":"Department of Radiology, \u201cMaggiore della Carit\u00e0\u201d Hospital, University of Piemonte Orientale (UPO), Via Solaroli 17, 28100 Novara, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alessio","family":"Pasch\u00e8","sequence":"additional","affiliation":[{"name":"Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09123 Cagliari, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8928-7666","authenticated-orcid":false,"given":"Pietro","family":"Danna","sequence":"additional","affiliation":[{"name":"Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09123 Cagliari, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luca","family":"Saba","sequence":"additional","affiliation":[{"name":"Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09123 Cagliari, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Armin","family":"Mehmedovi\u0107","sequence":"additional","affiliation":[{"name":"Department of Radiology, University Hospital for Infectious Diseases, 10000 Zagreb, Croatia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0189-8612","authenticated-orcid":false,"given":"Gavino","family":"Faa","sequence":"additional","affiliation":[{"name":"Department of Pathology, Azienda Ospedaliero Universitaria (A.O.U.), 09124 Cagliari, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Inder","family":"Singh","sequence":"additional","affiliation":[{"name":"Stroke Diagnostic and Monitoring Division, AtheroPoint\u2122, Roseville, CA 95661, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Monika","family":"Turk","sequence":"additional","affiliation":[{"name":"The Hanse-Wissenschaftskolleg Institute for Advanced Study, 27753 Delmenhorst, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paramjit","family":"Chadha","sequence":"additional","affiliation":[{"name":"Stroke Diagnostic and Monitoring Division, AtheroPoint\u2122, Roseville, CA 95661, 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