用AI提升眼底图糖尿病视网膜病变检测精度
Diabetic Retinopathy Detection Based on Convolutional Neural Networks with SMOTE and CLAHE Techniques Applied to Fundus Images
- 结合SMOTE与CLAHE增强数据，训练CNN模型识别眼底图像
- 二分类准确率达99.55%，多分类准确率95.26%
- 适合医疗AI研发者参考，尤其关注数据不平衡问题
糖尿病视网膜病变（DR）是糖尿病患者眼部的主要并发症之一，若未能及时发现可能导致永久失明。本研究旨在评估人工智能（AI）在诊断DR方面的准确性。采用合成少数类过采样技术（SMOTE）对公开数据集APTOS 2019 Blindness Detection中的眼底图像进行处理，利用卷积神经网络（CNN）识别DR及其严重程度阶段。文献检索通过ScienceDirect、ResearchGate、Google Scholar和IEEE Xplore完成。二分类（正常0，病变1）的分类结果中，准确率为99.55%，精确率99.54%，召回率99.54%，F1分数99.54%；多分类（无DR 0，轻度1，中度2，重度3，增殖性DR 4）准确率为95.26%，精确率95.26%，召回率95.17%，F1分数95.23%。混淆矩阵评估显示，二分类准确率为99.68%，多分类为96.65%。研究结果表明，该方法在提升DR诊断准确性方面显著优于传统人工分析。
原文摘要 · Abstract (English)
Diabetic retinopathy (DR) is one of the major complications in diabetic patients' eyes, potentially leading to permanent blindness if not detected timely. This study aims to evaluate the accuracy of artificial intelligence (AI) in diagnosing DR. The method employed is the Synthetic Minority Over-sampling Technique (SMOTE) algorithm, applied to identify DR and its severity stages from fundus images using the public dataset "APTOS 2019 Blindness Detection." Literature was reviewed via ScienceDirect, ResearchGate, Google Scholar, and IEEE Xplore. Classification results using Convolutional Neural Network (CNN) showed the best performance for the binary classes normal (0) and DR (1) with an accuracy of 99.55%, precision of 99.54%, recall of 99.54%, and F1-score of 99.54%. For the multiclass classification No_DR (0), Mild (1), Moderate (2), Severe (3), Proliferate_DR (4), the accuracy was 95.26%, precision 95.26%, recall 95.17%, and F1-score 95.23%. Evaluation using the confusion matrix yielded results of 99.68% for binary classification and 96.65% for multiclass. This study highlights the significant potential in enhancing the accuracy of DR diagnosis compared to traditional human analysis
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