COVLIAS 2.0-cXAI: Cloud-Based Explainable Deep Learning System for COVID-19 Lesion Localization in Computed Tomography Scans

Suri J. S.;Agarwal S.;Chabert G. L.;Carriero A.;Pasche A.;Danna P. S. C.;Saba L.;Mehmedovic A.;Faa G.;Singh I. M.;Turk M.;Chadha P. S.;Johri A. M.;Khanna N. N.;Mavrogeni S.;Laird J. R.;Pareek G.;Miner M.;Sobel D. W.;Balestrieri A.;Sfikakis P. P.;Tsoulfas G.;Protogerou A. D.;Misra D. P.;Agarwal V.;Kitas G. D.;Teji J. S.;Al-Maini M.;Dhanjil S. K.;Nicolaides A.;Sharma A.;Rathore V.;Fatemi M.;Alizad A.;Krishnan P. R.;Nagy F.;Ruzsa Z.;Fouda M. M.;Naidu S.;Viskovic K.;Kalra M. K.

2022-01-01

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Abstract

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 “COVLIAS 2.0-cXAI” 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 < 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.