Deep Learning Models Based on Weakly Supervised Learning and Clustering Visualization for Disease Diagnosis

Abstract

The coronavirus disease 2019 (COVID-19) has severely disrupted both human life and the health care system. Timely diagnosis and treatment have become increasingly important; however, the distribution and size of lesions vary widely among individuals, making it challenging to accurately diagnose the disease. This study proposed a deep-learning disease diagnosis model based on weakly supervised learning and clustering visualization (W_CVNet) that fused classification with segmentation. First, the data were preprocessed. An optimizable weakly supervised segmentation preprocessing method (O-WSSPM) was used to remove redundant data and solve the category imbalance problem. Second, a deep-learning fusion method was used for feature extraction and classification recognition. A dual asymmetric complementary bilinear feature extraction method (D-CBM) was used to fully extract complementary features, which solved the problem of insufficient feature extraction by a single deep learning network. Third, an unsupervised learning method based on Fuzzy C-Means (FCM) clustering was used to segment and visualize COVID-19 lesions enabling physicians to accurately assess lesion distribution and disease severity. In this study, 5-fold cross-validation methods were used, and the results showed that the network had an average classification accuracy of 85.8%, outperforming six recent advanced classification models. W_CVNet can effectively help physicians with automated aid in diagnosis to determine if the disease is present and, in the case of COVID-19 patients, to further predict the area of the lesion.