Recognition and Classification of Glomerular Pathological Images Based on Deep Learning

Abstract

The identification and classification of glomeruli in pathological sections is the key to diagnosing the degree and type of renal lesions. In order to solve the problem of glomerular recognition and classification, a complete glomerular detection and classification framework based on deep learning is designed. Glomeruli are detected and classified in the entire slice image. The framework includes four stages of glomerular recognition. In the first stage of scanning window generation, a new network framework, RGNet, is designed to initially deter948 计算机辅助设计与图形学学报 第 33 卷 mine the possible location of glomeruli. In the second stage of detection and coarse classification, Faster R-CNN is improved for glomerular data. In the third stage, the NMS-Lite algorithm is designed based on the NMS algorithm to merge the detected glomeruli. In the fourth stage of fine classification, two neural networks are trained using data augmentation to classify the degree of glomerular lesions. The experimental results has show that the glomerulus detection method proposed in this paper has achieved comparable accuracy on the test set with similar methods, and to a certain extent solves the problem that similar types of glomeruli are difficult to dis-