Curability of diabetic retinopathy (DR) abnormalities highly rely on regular monitoring, early-stage diagnosis and timely treatment. Detection and analysis of variation in eye images can help the patient to take the early action before progression of the disease. Vision loss can be effectively prevented by automated diagnostic system that assist the ophthalmologists who otherwise practice manual lesion detection processes which are tedious and time-consuming. This paper proposes a hierarchical severity level grading (HSG) system for the detection and classification of DR ailments. The retinal fundus images in the proposed HSG system are categorized as grade 0 (indicating Non-DR class) and DR severity grades 1, 2, 3 depending upon the number of anomalies; microaneurysms and haemorrhages in the fundus images. The challenge of retinal landmark segmentation, DR retinal discrimination and DR severity grading have been addressed in this work contributing to the novelty of the proposed approach. For non-DR and DR classification, the proposed system achieves an overall accuracy of 98.10% by SVM classifier and 100% by kNN classifier. Hierarchal discrimination into further grades of abnormalities resulted in accuracy values of 95.68% and 92.60% with SVM classifier using Gaussian kernel and, 97.90% and 95.30% employing fine kNN classifier. The HSG system demonstrates a clear improvement in accuracy with significantly less computational time comparative to the other state-of-the-art methods when applied to the MESSIDOR dataset. IDRiD dataset is also evaluated for performance validation of the proposed HSG system yielding a maximum of 94.00% classification accuracy using a kNN classifier with a computational time of 0.67 s.

糖尿病性视网膜病（DR）异常的可治愈性高度依赖于定期监测，早期诊断和及时治疗。对眼睛图像变化的检测和分析可以帮助患者在疾病发展之前采取早期行动。通过自动诊断系统可以有效地防止视力下降，该系统可以帮助眼科医生进行人工的病变检测过程，而这些过程既繁琐又费时。本文提出了一种用于DR疾病检测和分类的分级严重程度分级（HSG）系统。拟议的HSG系统中的视网膜眼底图像根据异常的数目分为0级（指示非DR级）和DR严重度等级1、2、3。眼底图像中有微动脉瘤和出血。这项工作已解决了视网膜界标分割，DR视网膜歧视和DR严重程度分级的挑战，从而为所提出的方法带来了新颖性。对于非DR和DR分类，所提出的系统通过SVM分类器可达到98.10％的整体精度，通过kNN分类器可实现100％的整体精度。使用高斯核的SVM分类器，对异常的进一步等级进行层次区分，得出的准确度值分别为95.68％和92.60％，使用精细kNN分类器的准确度值为97.90％和95.30％。与应用于MESSIDOR数据集的其他最新方法相比，HSG系统显示出明显的准确性提高，而计算时间却大大减少。还对IDRiD数据集进行了评估，以对提议的HSG系统进行性能验证，最多可产生94个。