Diabetic Retinopathy is a retinal abnormality which is characterized by progressive damage to the retina, eventually leading to irreversible blindness. In this paper, we propose an aggregated residual transformation‐based model for automatic multistage classification of diabetic retinopathy. The proposed model obtains 99.68% overall classification accuracy, 99.68% sensitivity, 99.89% specificity and 99.68% precision without overfitting on the MESSIDOR dataset. Further, the model obtains an accuracy of 99.89% for stage 0, 99.89% for stage 1, 99.68% for stage 2 and 99.89% for stage 3 of diabetic retinopathy. In comparison to residual network, the model shows an overall accuracy gain of 0.52%. The model also ensures an overall improvement of more than 6% in accuracy, 1.2% in sensitivity and 2.43 % in specificity when compared to best results reported in the literature. The proposed work outperforms the existing methods and achieves state‐of‐the‐art results for the multistage classification of diabetic retinopathy.

中文翻译：

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聚合残余转化网络用于糖尿病视网膜病变的多阶段分类

糖尿病性视网膜病是一种视网膜异常，其特征在于对视网膜的进行性损伤，最终导致不可逆的失明。在本文中，我们提出了一种基于聚集残差变换的模型，用于糖尿病性视网膜病变的自动多阶段分类。所提出的模型在不过度拟合MESSIDOR数据集的情况下获得了99.68％的总体分类精度，99.68％的灵敏度，99.89％的特异性和99.68％的精度。此外，该模型对于糖尿病性视网膜病变的0期准确度为99.89％，对于1期准确度为99.89％，对于2期准确度为99.68％，对于3期准确度为99.89％。与残差网络相比，该模型显示总体精度提高了0.52％。该模型还确保整体精度提高6％以上，灵敏度提高1.2％，并提高2。与文献报道的最佳结果相比，特异性为43％。拟议的工作优于现有方法，并获得了糖尿病视网膜病变多阶段分类的最新结果。