EYE-DEX自动识别10种眼病，准确率达92.36%，并可视化诊断依据。
EYE-DEX: Eye Disease Detection and EXplanation System
- 基于VGG16微调，结合大规模眼底图像数据集进行疾病分类。
- 在21,577张图像上实现92.36%的测试准确率，优于现有模型。
- 融合Grad-CAM生成可解释热力图，提升医生对AI判断的信任。
视网膜疾病诊断对预防视力丧失和减轻社会经济负担至关重要。全球超过22亿人受不同程度视力损伤影响，每年导致约4110亿美元生产力损失。传统眼科医生手动评级眼底图像耗时且主观性强。深度学习已革新医学诊断，实现视网膜图像自动化分析并达到专家级表现。本研究提出EYE-DEX，一个用于分类10种视网膜疾病的自动化框架，基于包含21,577张眼底图像的大型视网膜疾病数据集。我们对比了三种预训练卷积神经网络模型——VGG16、VGG19与ResNet50，其中微调后的VGG16取得92.36%的全局测试准确率，达到当前最优水平。为增强透明度与可解释性，集成梯度加权类激活映射（Grad-CAM）技术生成视觉解释，突出病变区域，从而提升临床医生对AI辅助诊断的信任与可靠性。
原文摘要 · Abstract (English)
Retinal disease diagnosis is critical in preventing vision loss and reducing socioeconomic burdens. Globally, over 2.2 billion people are affected by some form of vision impairment, resulting in annual productivity losses estimated at $411 billion. Traditional manual grading of retinal fundus images by ophthalmologists is time-consuming and subjective. In contrast, deep learning has revolutionized medical diagnostics by automating retinal image analysis and achieving expert-level performance. In this study, we present EYE-DEX, an automated framework for classifying 10 retinal conditions using the large-scale Retinal Disease Dataset comprising 21,577 eye fundus images. We benchmark three pre-trained Convolutional Neural Network (CNN) models--VGG16, VGG19, and ResNet50--with our finetuned VGG16 achieving a state-of-the-art global benchmark test accuracy of 92.36%. To enhance transparency and explainability, we integrate the Gradient-weighted Class Activation Mapping (Grad-CAM) technique to generate visual explanations highlighting disease-specific regions, thereby fostering clinician trust and reliability in AI-assisted diagnostics.
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