用序数回归提升糖尿病视网膜病变分级准确率
Stage Aware Diagnosis of Diabetic Retinopathy via Ordinal Regression
- 基于序数回归建模，融合绿通道提取等预处理方法
- 在APTOS-2019数据集上达到0.8992的加权肯德尔系数
- 适合眼科筛查与自动化诊断系统开发人员参考
糖尿病视网膜病变（DR）是近年来可预防性失明的主要原因。通过及时筛查和干预，可避免不可逆损伤。本文提出一种基于序数回归的DR检测框架，使用APTOS-2019眼底图像数据集。采用绿通道提取、噪声掩码和CLAHE的组合预处理方法，以突出与DR分类相关的关键特征。模型性能通过加权肯德尔系数（QWK）评估，重点关注结果与临床分级的一致性。该序数回归方法在APTOS数据集上取得0.8992的QWK得分，创下新基准。
原文摘要 · Abstract (English)
Diabetic Retinopathy (DR) has emerged as a major cause of preventable blindness in recent times. With timely screening and intervention, the condition can be prevented from causing irreversible damage. The work introduces a state-of-the-art Ordinal Regression-based DR Detection framework that uses the APTOS-2019 fundus image dataset. A widely accepted combination of preprocessing methods: Green Channel (GC) Extraction, Noise Masking, and CLAHE, was used to isolate the most relevant features for DR classification. Model performance was evaluated using the Quadratic Weighted Kappa, with a focus on agreement between results and clinical grading. Our Ordinal Regression approach attained a QWK score of 0.8992, setting a new benchmark on the APTOS dataset.
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