Automated detection of diabetic retinopathy using convolutional neural networks on a small dataset

Abstract

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.