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

IF 3.9 2区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Xiang-Yong Kong, Xin-Shen Zhao, Xiao-Han Sun, Ping Wang, Ying Wu, Rui-Yang Peng, Qi-Yuan Zhang, Yu-Ze Wang, Rong Li, Yi-Heng Yang, Ying-Rui Lv
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引用次数: 0

Abstract

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.

Abstract Image

使用具有改进SE ResNet模块的卷积神经网络对儿童肾小球病理学图像进行分类。
根据组织学切片对肾小球病理进行分类是诊断肾脏疾病类型和程度的关键。为了解决儿童肾小球病变分类中的问题,设计了一个基于深度学习的完整肾小球分类框架来检测和分类肾小球病理。提出了一种融合Resnet和Senet的神经网络(RS INet),并实现了肾小球分类算法,实现了肾小球病理学的高精度分类。在保证网络性能的前提下,通过将原始Resnet残差块的卷积层转换为具有较小参数和减少网络参数的卷积块,对SE Resnet进行了改进。实验结果表明,与其他分类算法相比,我们的算法在区分系膜增殖性肾小球肾炎(MsPGN)、新月形肾小球肾炎(CGN)和肾小球硬化(GS)与正常肾小球(normal)方面表现最好。准确率分别为0.960、0.940、0.937和0.968。这表明,本研究中提出的分类算法能够以更高的精度识别肾小球病变,并将相似的肾小球病变相互区分开来。
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来源期刊
Interdisciplinary Sciences: Computational Life Sciences
Interdisciplinary Sciences: Computational Life Sciences MATHEMATICAL & COMPUTATIONAL BIOLOGY-
CiteScore
8.60
自引率
4.20%
发文量
55
期刊介绍: Interdisciplinary Sciences--Computational Life Sciences aims to cover the most recent and outstanding developments in interdisciplinary areas of sciences, especially focusing on computational life sciences, an area that is enjoying rapid development at the forefront of scientific research and technology. The journal publishes original papers of significant general interest covering recent research and developments. Articles will be published rapidly by taking full advantage of internet technology for online submission and peer-reviewing of manuscripts, and then by publishing OnlineFirstTM through SpringerLink even before the issue is built or sent to the printer. The editorial board consists of many leading scientists with international reputation, among others, Luc Montagnier (UNESCO, France), Dennis Salahub (University of Calgary, Canada), Weitao Yang (Duke University, USA). Prof. Dongqing Wei at the Shanghai Jiatong University is appointed as the editor-in-chief; he made important contributions in bioinformatics and computational physics and is best known for his ground-breaking works on the theory of ferroelectric liquids. With the help from a team of associate editors and the editorial board, an international journal with sound reputation shall be created.
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