Classification of Glomerular Pathology Images in Children Using Convolutional Neural Networks with Improved SE-ResNet Module

• Published in: Interdisciplinary sciences : computational life sciences Vol. 15; no. 4; pp. 602 - 615
• Main Authors: Kong, Xiang-Yong, Zhao, Xin-Shen, Sun, Xiao-Han, Wang, Ping, Wu, Ying, Peng, Rui-Yang, Zhang, Qi-Yuan, Wang, Yu-Ze, Li, Rong, Yang, Yi-Heng, Lv, Ying-Rui
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 01.12.2023; Springer Nature B.V
• Subjects: Algorithms; Artificial neural networks; Biomedical and Life Sciences; Classification; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Appl. in Life Sciences; Deep learning; Glomerulonephritis; Glomerulus; Health Sciences; Histology; Kidney diseases; Lesions; Life Sciences; Machine learning; Mathematical and Computational Physics; Medicine; Neural networks; Original Research Article; Parameters; Pathology; Statistics for Life Sciences; Theoretical; Theoretical and Computational Chemistry; Children; Convolutional neural networks; Pathology images; Glomerular; Image classification
• ISSN: 1913-2751; 1867-1462; 1867-1462
• DOI: 10.1007/s12539-023-00579-7

Classification of glomerular pathology based on histology sections is the key to diagnose the type and degree of kidney diseases. To address problems in the classification of glomerular lesions in children, a deep learning-based complete glomerular classification framework was designed to detect and classify glomerular pathology. A neural network integrating Resnet and Senet (RS-INet) was proposed and a glomerular classification algorithm implemented to achieve high-precision classification of glomerular pathology. SE-Resnet was applied with improvement by transforming the convolutional layer of the original Resnet residual block into a convolutional block with smaller parameters as well as reduced network parameters on the premise of ensuring network performance. Experimental results showed that our algorithm had the best performance in differentiating mesangial proliferative glomerulonephritis (MsPGN), crescent glomerulonephritis (CGN), and glomerulosclerosis (GS) from normal glomerulus (Normal) compared with other classification algorithms. The accuracy rates were 0.960, 0.940, 0.937, and 0.968, respectively. This suggests that the classification algorithm proposed in the present study is able to identify glomerular lesions with a higher precision, and distinguish similar glomerular pathologies from each other. Graphical Abstract