用YOLOv8和SVM自动识别糖尿病视网膜病变，提升诊断效率。
Diagnosis of diabetic retinopathy using machine learning & deep learning technique
- 用YOLOv8定位视盘、视杯和病灶等关键区域
- 通过SVM分类实现分期诊断，准确率达84%
- 适合基层或偏远地区快速筛查使用
眼底图像广泛用于糖尿病视网膜病变、青光眼和年龄相关性黄斑变性等眼病的诊断。但人工分析眼底图像耗时且易出错。本文提出一种结合目标检测与机器学习分类的新方法：先用YOLOv8在眼底图像中定位视盘、视杯及病灶等感兴趣区域（ROIs），再基于是否存在渗出物、微动脉瘤、出血等病理特征，利用支持向量机（SVM）对这些区域进行糖尿病视网膜病变分期分类。该方法在眼底图像检测中达到84%的准确率，具备高效率，可应用于远程地区的视网膜疾病初筛。
原文摘要 · Abstract (English)
Fundus images are widely used for diagnosing various eye diseases, such as diabetic retinopathy, glaucoma, and age-related macular degeneration. However, manual analysis of fundus images is time-consuming and prone to errors. In this report, we propose a novel method for fundus detection using object detection and machine learning classification techniques. We use a YOLO_V8 to perform object detection on fundus images and locate the regions of interest (ROIs) such as optic disc, optic cup and lesions. We then use machine learning SVM classification algorithms to classify the ROIs into different DR stages based on the presence or absence of pathological signs such as exudates, microaneurysms, and haemorrhages etc. Our method achieves 84% accuracy and efficiency for fundus detection and can be applied for retinal fundus disease triage, especially in remote areas around the world.
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