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

作     者：Jingyao Liu Qinghe Feng Jiashi Zhao Yu Miao Wei He Weili Shi Zhengang Jiang 

作者机构：School of Computer and Information EngineeringChuzhou UniversityChuzhou239000China School of Computer Science and TechnologyChangchun University of Science and TechnologyChangchun130022China Zhongshan Institute of Changchun University of Science and TechnologyZhongshanChina School of Intelligent EngineeringHenan Institute of TechnologyXinxiang453003China 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2023年第76卷第9期

页      面：2649-2665页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：funded by the Open Foundation of Anhui EngineeringResearch Center of Intelligent Perception and Elderly Care,Chuzhou University(No.2022OPA03) the Higher EducationNatural Science Foundation of Anhui Province(No.KJ2021B01)and the Innovation Team Projects of Universities in Guangdong(No.2022KCXTD057) 

主　　题：Classification COVID-19 deep learning segmentation unsupervised learning weakly supervised 

摘      要：The coronavirus disease 2019(COVID-19)has severely disrupted both human life and the health care *** 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 *** study proposed a deep-learning disease diagnosismodel based onweakly supervised learning and clustering visualization(W_CVNet)that fused classification with ***,the data were *** optimizable weakly supervised segmentation preprocessing method(O-WSSPM)was used to remove redundant data and solve the category imbalance ***,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 ***,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 *** 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.