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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: email

Computers, Materials & Continua 2023, 76(3), 2649-2665. https://doi.org/10.32604/cmc.2023.038891

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.

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Cite This Article

J. Liu, Q. Feng, J. Zhao, Y. Miao, W. He et al., "Deep learning models based on weakly supervised learning and clustering visualization for disease diagnosis," Computers, Materials & Continua, vol. 76, no.3, pp. 2649–2665, 2023.



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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