@Article{cmc.2023.038891,
AUTHOR = {Jingyao Liu, Qinghe Feng, Jiashi Zhao, Yu Miao, Wei He, Weili Shi, Zhengang Jiang},
TITLE = {Deep Learning Models Based on Weakly Supervised Learning and Clustering Visualization for Disease Diagnosis},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {76},
YEAR = {2023},
NUMBER = {3},
PAGES = {2649--2665},
URL = {http://www.techscience.com/cmc/v76n3/54314},
ISSN = {1546-2226},
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 crossvalidation 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.},
DOI = {10.32604/cmc.2023.038891}
}