@Article{Yun2025,
journal="Folia Neuropathologica",
issn="1641-4640",
volume="63",
number="3",
year="2025",
title="Application of a convolutional neural network model to construct an automatic, AI-based identification system for rat kidney tissue microscopic images",
abstract="The aim was to develop an artificial intelligence (AI)-based identification system using a deep learning method based on convolutional neural networks to analyze pathological kidney images. We constructed a rat chronic kidney disease (CKD) model and used different drug interventions to study the pathological changes in the kidneys, which is convenient for machine in-depth learning and the construction of a recognition system. The reliability and credibility of the system were assessed by a blind comparative analysis. Microscopic image recognition and classification: Five pathological groups were subjected to three types of staining to obtain 15 image groups. Fifty jpg images were captured from each image group, so that 750 images were captured for training. The average hard disk space per image was 354 kb. Because the input image was 1000 pixels wide with a large resolution, multiple sampling was required to extract feature information. This implies that the level of span multiplication greatly affected the performance of the network during sampling. First, the ResPool samples with the same number of channels, followed by the output of a convolution layer, were assembled as the incremental channel. Thus, the feature extraction network of this work successfully implemented the idea of residual learning and did not introduce errors in the deep network to achieve the expected effect. The neural network model of this study designed a ResPool sampling structure based on the idea of residual learning, completed glomerular instance segmentation and damage analysis, and improved the accuracy of image recognition tasks.",
author="Yun, Chen
and Zha, Ai-Yun
and He, Xiaofan
and Xu, Suhua
and Tang, Xiaohong
and Li, Qiang
and Yin, Lianghong
and Luan, Shaodong",
pages="289--303",
doi="10.5114/fn.2025.153859",
url="http://dx.doi.org/10.5114/fn.2025.153859"
}