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一个全面的CNN模型用于使用OCT进行与年龄相关的斑点退化分类:集成初始模块,SE块和ConvMixer

Elif Yusufoğlu¹, Hüseyin Fırat², Hüseyin Üzen³

¹Department of Ophthalmology, Elazig Fethi Sekin City Hospital, 23100 Elazig, Türkiye.

Diagnostics (Basel, Switzerland)

|January 8, 2025

概括

一种新的深度学习 (DL) 方法使用光学连贯断层扫描 (OCT) 扫描精确诊断与年龄相关的黄斑变性 (AMD). 这种人工智能方法显示出高性能,可能使得AMD患者的早期检测和干预成为可能.

科学领域:

眼科和医学成像学
医疗保健中的人工智能
计算病理学计算病理学

背景情况:

与年龄相关的黄斑变性 (AMD) 是老年人视力丧失的主要原因,通常缺乏早期诊断症状.
深度学习 (DL) 模型,特别是卷积神经网络 (CNN),显示出从光学连贯断层扫描 (OCT) 扫描中诊断AMD的前景.
目前的诊断方法可能不够有效或足够准确用于早期AMD检测.

研究的目的:

引入一种基于CNN的新型深度学习方法,以提高AMD诊断的计算效率和准确性.
评估拟议方法的诊断性能在私人和公共的OCT数据集上.
为了将拟议的方法与现有的深度学习方法进行对比,用于AMD分类.

主要方法:

开发一种新的深度学习模型,集成修改后的Inception模块,深度挤压和激发块和ConvMixer架构.
在私人数据集 (2316张图像) 和公共 Noor 数据集上对模型的评估.
使用关键指标进行绩效评估:准确性,精度,回忆和F1分数.

主要成果:

拟议的方法在私人数据集上取得了高性能:97.98%的准确性,97.95%的精度,97.77%的回忆率和97.86%的F1得分.
在公开的Noor数据集中,该方法在所有评估指标中达到100%.
该模型在测试的数据集上优于现有的AMD诊断深度学习方法.

结论:

基于人工智能的系统,特别是拟议的DL模型,显示出对准确的AMD诊断有很大的潜力.
该方法的高级特征提取能力可以促进早期检测和干预,改善患者的治疗结果.
未来的研究应该专注于外部临床验证,以解决数据集的局限性并确认可概括性.

关键词:

这是一个ConvMixer.与年龄相关的黄斑变性.深度挤压和激发阻断器修改后的初始模块开始模块.光学连贯性断层扫描 (optical coherence tomography) 是一种光学连贯性断层扫描技术.

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