Open Access
ARTICLE
Deep Learning Models Based on Weakly Supervised Learning and Clustering Visualization for Disease Diagnosis
Jingyao Liu1,2, Qinghe Feng4, Jiashi Zhao2,3, Yu Miao2,3, Wei He2, Weili Shi2,3, Zhengang Jiang2,3,*
1 School of Computer and Information Engineering, Chuzhou University, Chuzhou, 239000, China
2 School of Computer Science and Technology, Changchun University of Science and Technology, Changchun,
130022, China
3 Zhongshan Institute of Changchun University of Science and Technology, Zhongshan, China
4 School of Intelligent Engineering, Henan Institute of Technology, Xinxiang, 453003, China
* Corresponding Author: Zhengang Jiang. Email:
Computers, Materials & Continua 2023, 76(3), 2649-2665. https://doi.org/10.32604/cmc.2023.038891
Received 02 January 2023; Accepted 17 May 2023; Issue published 08 October 2023
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.
Keywords
Cite This Article
APA Style
Liu, J., Feng, Q., Zhao, J., Miao, Y., He, W. et al. (2023). Deep learning models based on weakly supervised learning and clustering visualization for disease diagnosis. Computers, Materials & Continua, 76(3), 2649-2665. https://doi.org/10.32604/cmc.2023.038891
Vancouver Style
Liu J, Feng Q, Zhao J, Miao Y, He W, Shi W, et al. Deep learning models based on weakly supervised learning and clustering visualization for disease diagnosis. Comput Mater Contin. 2023;76(3):2649-2665 https://doi.org/10.32604/cmc.2023.038891
IEEE Style
J. Liu et al., "Deep Learning Models Based on Weakly Supervised Learning and Clustering Visualization for Disease Diagnosis," Comput. Mater. Contin., vol. 76, no. 3, pp. 2649-2665. 2023. https://doi.org/10.32604/cmc.2023.038891