用深度学习与可解释性技术提升眼底OCT图像疾病分类精度
RetinaVision: XAI-Driven Augmented Regulation for Precise Retinal Disease Classification using deep learning framework
- 基于Xception和InceptionV3模型结合数据增强进行分类
- Xception达95.25%准确率，优于InceptionV3的94.82%
- 集成GradCAM/LIME提升可解释性，适合临床辅助诊断
早期精准分类视网膜疾病对预防视力丧失及指导临床管理至关重要。本研究提出一种基于深度学习的视网膜疾病分类方法，使用来自Retinal OCT Image Classification - C8数据集的24,000张标注OCT图像（涵盖8种疾病），图像尺寸统一为224x224 px。采用Xception与InceptionV3两种卷积神经网络架构，并应用CutMix、MixUp等数据增强策略以提升模型泛化能力。同时引入GradCAM与LIME进行可解释性评估。研究结果显示，Xception模型表现最优，准确率达95.25%，InceptionV3为94.82%。研究成果表明，深度学习可有效实现OCT图像中的视网膜疾病分类，并强调在临床应用中兼顾准确性与可解释性的重要性。该系统已通过名为RetinaVision的网页应用实现实时部署。
原文摘要 · Abstract (English)
Early and accurate classification of retinal diseases is critical to counter vision loss and for guiding clinical management of retinal diseases. In this study, we proposed a deep learning method for retinal disease classification utilizing optical coherence tomography (OCT) images from the Retinal OCT Image Classification - C8 dataset (comprising 24,000 labeled images spanning eight conditions). Images were resized to 224x224 px and tested on convolutional neural network (CNN) architectures: Xception and InceptionV3. Data augmentation techniques (CutMix, MixUp) were employed to enhance model generalization. Additionally, we applied GradCAM and LIME for interpretability evaluation. We implemented this in a real-world scenario via our web application named RetinaVision. This study found that Xception was the most accurate network (95.25%), followed closely by InceptionV3 (94.82%). These results suggest that deep learning methods allow effective OCT retinal disease classification and highlight the importance of implementing accuracy and interpretability for clinical applications.
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