用轻量CNN高效识别糖尿病视网膜病变和黄斑孔
Lightweight Convolutional Neural Networks for Retinal Disease Classification
- 采用MobileNet和NASNetMobile模型，适配小样本眼科图像
- 在RFMiD数据集上达90.8%准确率，优于基准模型
- 适合医疗AI落地，助力基层眼病早筛
糖尿病视网膜病变（DR）和黄斑孔（MH）是严重威胁视力的常见眼病，全球影响数百万患者。早期发现至关重要：DR会损伤视网膜血管，可能导致失明；而MH则破坏中心视野，影响阅读与人脸辨识。本文采用两种轻量高效的卷积神经网络架构——MobileNet和NASNetMobile，对正常、DR和MH三类眼底图像进行分类。模型在包含3,200张眼底图像的RFMiD数据集上训练，经过图像缩放、归一化及增强等预处理。针对数据稀缺问题，研究结合迁移学习与数据增强技术，提升模型泛化能力。实验结果表明，MobileNetV2达到最高准确率90.8%，优于NASNetMobile的89.5%。该成果验证了卷积神经网络在眼病分类中的有效性，为人工智能辅助眼科诊断与早期干预提供可行方案。
原文摘要 · Abstract (English)
Retinal diseases such as Diabetic Retinopathy (DR) and Macular Hole (MH) significantly impact vision and affect millions worldwide. Early detection is crucial, as DR, a complication of diabetes, damages retinal blood vessels, potentially leading to blindness, while MH disrupts central vision, affecting tasks like reading and facial recognition. This paper employed two lightweight and efficient Convolution Neural Network architectures, MobileNet and NASNetMobile, for the classification of Normal, DR, and MH retinal images. The models were trained on the RFMiD dataset, consisting of 3,200 fundus images, after undergoing preprocessing steps such as resizing, normalization, and augmentation. To address data scarcity, this study leveraged transfer learning and data augmentation techniques, enhancing model generalization and performance. The experimental results demonstrate that MobileNetV2 achieved the highest accuracy of 90.8%, outperforming NASNetMobile, which achieved 89.5% accuracy. These findings highlight the effectiveness of CNNs in retinal disease classification, providing a foundation for AI-assisted ophthalmic diagnosis and early intervention.
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