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整合轻量级卷积神经网络与通道注意力和适应性空间注意力,用于基于OCT的视网膜疾病分类

Md Rayhan Ahmed¹, Mohamed S Shehata¹, Patricia Lasserre¹

¹Irving K. Barber Faculty of Science, University of British Columbia, Kelowna, BC, V1V 1V8, Canada.

Computers in biology and medicine

|April 2, 2025

概括

一个轻量级的卷积神经网络LOCT-Net有效地从OCT B扫描中对视网膜疾病进行分类. 这种新的架构以最小的参数实现了高精度,超过了现有的方法.

科学领域:

眼科医生眼科眼科
医疗成像医学成像
人工智能的人工智能

背景情况:

从光学一致性断层扫描 (OCT) B扫描进行视网膜疾病分类对于诊断至关重要.
现有的深度学习模型经常面临计算成本和特征提取复杂性的挑战.

研究的目的:

提出LOCT-Net,一个有效和轻量级的上下文卷积神经网络用于视网膜疾病分类.
加强特征提取和精细化,以提高使用OCT B扫描的诊断准确度.

主要方法:

开发了LOCT-Net架构,嵌套剩余块和使用扩展集成深度条形卷曲的多尺度特征增强.
整合了一个新的功能改进网络,带有知情通道注意力和自适应空间注意力模块.
利用后可解释的人工智能方法来解释模型的可解释性.

主要成果:

在六个基准数据集 (OCTID,OCTDL,DUKE,SD-OCT Noor,NEH,UCSD) 中,LOCT-Net实现了最先进的性能.
该模型展示了性能和计算成本之间的高效平衡,只有2.32M可训练参数.
获得了高的F1分数,包括92.98% (OCTID),92.34% (OCTDL),100% (DUKE),99.58% (SD-OCT Noor),94.50% (NEH) 和97.14% (UCSD). 获得了高的F1分数,包括92.98% (OCTID),92.34% (OCTDL),100% (DUKE),99.58% (SD-OCT Noor),94.50% (NEH) 和97.14% (UCSD) 的F1分数.

结论:

LOCT-Net提供了一种高效和计算效率的解决方案,用于使用OCT B扫描进行视网膜疾病分类.
拟议的架构成功地解决了该领域的关键挑战,超过了以前的先进方法.
该模型的可解释性特征有助于可信的临床决策.

关键词:

适应空间注意力适应性空间注意力卷积神经网络是一种卷积神经网络.透信息的注意力道注意力道.可解释的人工智能光学连贯性断层扫描技术.视网膜疾病分类视网膜疾病分类

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