COVLIAS 3.0: cloud-based quantized hybrid UNet3+ deep learning for COVID-19 lesion detection in lung computed tomography - Agarwal, Sushant; Saxena, Sanjay; Carriero, Alessandro; Chabert, Gian Luca; Ravindran, Gobinath; Paul, Sudip; Laird, John R.; Garg, Deepak; Fatemi, Mostafa; Mohanty, Lopamudra; Dubey, Arun K.; Singh, Rajesh; et al. (Frontiers in Artificial Intelligence, 2024)
Background: Detection of COVID-19 lesion location and position in Computed Tomography (CT) scans is essential for understanding COVID severity for early diagnosis of the disease, especially in patients having high ground-glass opacities, consolidations, and crazy paving, where RT-PCR is ineffective. Radiologists find the manual method for lesion detection very challenging and tedious. Previously solo deep learning (SDL) was tried but with low to moderate-level performance. This study presents two cloud-based quantized deep learning UNet3+ hybrid (HDL) models which incorporated full-scale skip connections to enhance and improve the detections. Methodology: Annotations from expert radiologists were used to train one SDL (UNet3+), and two HDL models, namely, VGG-UNet3+ and ResNet-UNet3+. For accuracy, five-fold cross-validation protocols, training on 3500 CT scans, and testing on unseen 500 CT scans were adopted in the cloud framework. Two kinds of loss functions were used: Dice Similarity (DS) and binary cross-entropy (BCE). Performance was evaluated using (i) Area error, (ii) DS, (iii) Jaccard Index, (iii) Bland-Altman, and (iv) Correlation plots. Results: Among the two HDL models, ResNet-UNet3+ was superior to UNet3+ by 17% and 10% for Dice and BCE loss. The models were further compressed using quantization showing a percentage size reduction of 66.76%, 36.64%, and 46.23%, respectively, for UNet3+, VGG-UNet3+, and ResNet-UNet3+. Its stability and reliability were proved by statistical tests such as the Mann-Whitney, Paired t-Test, Wilcoxon test, and Friedman test all of which had a p<0.001. Conclusions: Full-scale skip connections of UNet3+ with VGG and ResNet in HDL framework proved the hypothesis showing powerful results improving the detection accuracy of COVID-19.