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在使用卷积神经网络与改进的SE-ResNet模块的儿童中分类淋巴细胞病理图像

Xiang-Yong Kong¹, Xin-Shen Zhao¹, Xiao-Han Sun¹

¹School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China.

Interdisciplinary sciences, computational life sciences

|July 31, 2023

概括

一个深度学习框架通过分析质病理学来准确地分类儿科脏疾病. 新的RS-INet模型在区分各种淋巴细胞病变方面实现了高精度,改善了诊断.

科学领域:

腎臟病學 (nephrology) 是一種醫學專業.
医疗成像医学成像
人工智能的人工智能

背景情况:

从组织学上准确地分类淋巴细胞病理,对于诊断脏疾病至关重要.
儿童病的分类带来了独特的挑战.
深度学习为自动化和精确的病理分析提供了潜力.

研究的目的:

开发一种基于深度学习的框架,用于对儿童的球病理进行分类.
使用集成的神经网络设计一个高精度的球分类算法.
为了解决目前用于识别和区分儿科球病变的方法的局限性.

主要方法:

一个新的神经网络,Resnet和Senet集成网络 (RS-INet) 被开发出来.
通过减少卷积层参数,同时保持性能,优化了SE-Resnet架构.
采用RS-INet模型实现了质分类算法.

主要成果:

RS-INet算法在区分半增殖性淋巴结核炎 (MsPGN),半月淋巴结核炎 (CGN) 和淋巴结核硬化 (GS) 与正常淋巴结核细胞方面表现出卓越的性能.
实现了高准确率:MsPGN为0.960,CGN为0.940,GS为0.937,正常为0.968.
该算法有效地区分了类似的淋巴细胞病理.

结论:

拟议的深度学习分类算法准确地识别了儿科脏疾病中的球损伤.
这一框架提高了复杂的球病理的诊断精度和差异化.
这项研究强调了人工智能在改善儿科病诊断方面的潜力.

关键词:

孩子孩子孩子孩子卷积神经网络是一种卷积神经网络.淋巴细胞的淋巴细胞.图像的分类图像的分类.病理学图像病理学图像

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