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.

翻译：视网膜疾病的早期准确分类对于对抗视力丧失和指导视网膜疾病的临床管理至关重要。本研究提出了一种利用光学相干断层扫描（OCT）图像进行视网膜疾病分类的深度学习方法，图像来源于视网膜OCT图像分类-C8数据集（包含涵盖八种病症的24,000张标注图像）。图像被调整为224x224像素，并在卷积神经网络架构：Xception和InceptionV3上进行了测试。采用了数据增强技术（CutMix、MixUp）以提升模型的泛化能力。此外，我们应用了GradCAM和LIME进行可解释性评估。我们通过名为RetinaVision的Web应用程序在真实场景中实现了该方法。本研究发现，Xception是准确率最高的网络（95.25%），InceptionV3紧随其后（94.82%）。这些结果表明，深度学习方法能够实现有效的OCT视网膜疾病分类，并强调了在临床应用中同时实现准确性和可解释性的重要性。