用机器学习分析眼底图像特征，自动分级糖尿病视网膜病变。
Diabetic Retinopathy Classification from Retinal Images using Machine Learning Approaches
- 提取渗出物、血管、微动脉瘤等病灶特征作为输入。
- 随机森林分类器达到76.5%准确率，93.3%特异性。
- 适合医疗辅助诊断场景，尤其关注早期筛查。
糖尿病视网膜病变是糖尿病常见并发症，早期可能无症状或仅有轻微视力问题，晚期可致失明。早期发现对防止失明至关重要。本文研究了渗出物、血管和微动脉瘤等病灶特征，用于识别健康、轻度非增殖期、中度非增殖期、重度非增殖期及增殖期五个阶段。采用支持向量机、随机森林和朴素贝叶斯分类器进行分类。实验结果表明，随机森林表现最优，准确率为76.5%，敏感性为77.2%，特异性达93.3%。
原文摘要 · Abstract (English)
Diabetic Retinopathy is one of the most familiar diseases and is a diabetes complication that affects eyes. Initially, diabetic retinopathy may cause no symptoms or only mild vision problems. Eventually, it can cause blindness. So early detection of symptoms could help to avoid blindness. In this paper, we present some experiments on some features of diabetic retinopathy, like properties of exudates, properties of blood vessels and properties of microaneurysm. Using the features, we can classify healthy, mild non-proliferative, moderate non-proliferative, severe non-proliferative and proliferative stages of DR. Support Vector Machine, Random Forest and Naive Bayes classifiers are used to classify the stages. Finally, Random Forest is found to be the best for higher accuracy, sensitivity and specificity of 76.5%, 77.2% and 93.3% respectively.
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