期刊
DIAGNOSTICS
卷 12, 期 6, 页码 -出版社
MDPI
DOI: 10.3390/diagnostics12061482
关键词
COVID-19 lesion; lung CT; Hounsfield units; glass ground opacities; hybrid deep learning; explainable AI; segmentation; classification; GRAD-CAM; Grad-CAM++; Score-CAM; FasterScore-CAM
This study presents a cloud-based explainable AI system, COVLIAS 2.0-cXAI, using four kinds of CAM models for lesion localization in lung CT scans. The system achieved high performance and stability, and received a high score in clinical settings.
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 similar to 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.
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