Diabetic retinopathy (DR) is a significant reason for the global increase in visual loss. Studies show that timely treatment can significantly bring down such incidents. Hence, it is essential to distinguish the stages and severity of DR to recommend needed medical attention. In this view, this paper presents DRISTI (Diabetic Retinopathy classIfication by analySing reTinal Images), where a hybrid deep learning model composed of VGG16 and capsule network is proposed, which yields statistically significant performance improvement over the state of the art. To validate our claim, we have reported detailed experimental and ablation studies. We have also created an augmented dataset to increase the APTOS dataset’s size and check how robust the model is. The five-class training and validation accuracy for the expanded dataset is \(99.21\%\) and \(75.50\%\). The two-class training and validation accuracy on augmented APTOS is \(99.96\%\) and \(97.05\%\). Extending the two-class model for the mixed dataset, we get a training and validation accuracy of \(99.92\%\) and \(91.43\%\), respectively. We have also performed cross-dataset and mixed dataset testing to demonstrate the efficiency of DRISTI.

糖尿病视网膜病变 (DR) 是全球视力丧失增加的重要原因。研究表明，及时治疗可以显着减少此类事件。因此，必须区分 DR 的阶段和严重程度，以推荐所需的医疗护理。在这个观点下，本文提出了 DRISTI（通过分析视网膜图像进行糖尿病视网膜病变分类），其中提出了由 VGG16 和胶囊网络组成的混合深度学习模型，与现有技术相比，该模型在统计上产生了显着的性能改进。为了验证我们的说法，我们报告了详细的实验和消融研究。我们还创建了一个增强数据集以增加 APTOS 数据集的大小并检查模型的稳健性。扩展数据集的五类训练和验证精度为\(99.21\%\)和\(75.50\%\)。增强型 APTOS 的二类训练和验证准确度为\(99.96\%\)和\(97.05\%\)。扩展混合数据集的二分类模型，我们得到训练和验证准确度分别为\(99.92\%\)和\(91.43\%\)。我们还进行了跨数据集和混合数据集测试，以证明 DRISTI 的效率。