Diabetic Retinopathy is a complication based on patients suffering from type-1 or type-2 diabetes. Early detection is essential as complication can lead to vision problems such as retinal detachment, vitreous hemorrhage and glaucoma. The principal stages of diabetic retinopathy are non-Proliferative diabetic retinopathy and Proliferative diabetic retinopathy. In this paper, we propose a transfer learning based CNN architecture on colour fundus photography that performs relatively well on a much smaller dataset of skewed classes of 3050 training images and 419 validation images in recognizing classes of Diabetic Retinopathy from hard exudates, blood vessels and texture. This model is extremely robust and lightweight, garnering a potential to work considerably well in small real time applications with limited computing power to speed up the screening process. The dataset was trained on Google Colab. We trained our model on 4 classes - I)No DR ii)Mild DR iii)Moderate DR iv)Proliferative DR, and achieved a Cohens Kappa score of 0.8836 on the validation set along with 0.9809 on the training set.

糖尿病性视网膜病是一种基于患有1型或2型糖尿病的患者的并发症。早期发现至关重要，因为并发症会导致视力问题，例如视网膜脱离，玻璃体出血和青光眼。糖尿病性视网膜病的主要阶段是非增生性糖尿病性视网膜病和增生性糖尿病性视网膜病。在本文中，我们提出了一种基于转移学习的彩色眼底摄影CNN架构，该结构在较小的3050个训练图像和419个验证图像的偏斜类数据集上表现较好，可以从硬性渗出液，血管和纹理中识别出糖尿病性视网膜病变。这个模型非常坚固轻巧，在有限的计算能力以加快筛选过程的小型实时应用中获得了很好的工作潜力。该数据集在Google Colab上进行了训练。我们在4个类别上训练了我们的模型-I）无DR ii）轻度DR iii）中度DR iv）增殖性DR，在验证集上的Cohens Kappa得分为0.8836，在训练集上的得分为0.9809。