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基于卷积神经网络的结直肠和前列腺瘤活检的自动分类使用多谱图像:系统发展研究

Remy Peyret¹, Duaa alSaeed², Fouad Khelifi¹

¹Northumbria University at Newcastle, Newcastle, United Kingdom.

JMIR bioinformatics and biotechnology

|June 27, 2024

概括

一种新型的卷积神经网络 (CNN) 系统从活检图像准确诊断结直肠和前列腺癌. 与手动分析相比,这种自动化方法显著减少了诊断错误和时间.

关键词:

这是分类分类的分类.结直肠瘤是什么意思卷积神经网络是一种卷积神经网络.图像处理是图像处理的过程.机器学习是机器学习.前列腺瘤的前列腺瘤.

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科学领域:

医疗成像医学成像
计算病理学计算病理学
在瘤学中使用人工智能

背景情况:

结肠直肠癌和前列腺癌是全球领先的男性癌症.
活检样本的手动组织学分析是耗时的,容易导致观察者变化.
目前用于这些癌症的诊断方法可能会影响可靠性和效率.

研究的目的:

开发一种用于结直肠和前列腺瘤诊断的自动化计算机系统.
提高诊断准确度,减少与手动病理分析相关的时间.
为了利用深度学习,从活检图像中改进癌症检测.

主要方法:

提出了一个新的卷积神经网络 (CNN) 架构.
该CNN模型旨在使用多光谱活检图像对结直肠和前列腺瘤进行分类.
关键的修改包括删除最后一个卷积块,并将每个层的过器减半.

主要成果:

拟议的CNN实现了高精度:前列腺99.8%,结直肠数据集99.5%.
该系统的性能优于预先训练有素的CNN和其他分类方法.
它消除了预处理的需要,并使用单一的CNN模型来完成整个任务.

结论:

开发的CNN架构在分类结直肠和前列腺瘤图像方面表现出卓越的性能.
该系统提供了一个更有效和可靠的替代手动病理检查.
拟议的CNN架构在计算上高效,不需要预处理图像,因此非常适合临床应用.