A Deep Learning Based Approach for the Classification of Diabetic Retinopathy in Human Retina

Abstract

Diabetic Retinopathy, a common diabetes complication causes damages to the blood vessels of light sensitive tissues in the human retina. Due to the limitations in the manual screening process, there exists a compelling requirement of an automated approach for the Diabetic Retinopathy screening which can be applied regularly and in abundance in any kind of a healthcare environment. This paper suggests a Deep Learning based automated approach to classify retinal fundus images into five major severity levels while focusing on achieving the optimal accuracy-efficiency balance in performance. In the classification task, a lightweight Convolutional Neural Network (CNN) model with only 6 convolutional layers was suggested to classify retinal fundus images to five major severity levels. CNN refinements such as Hyperparameter Tuning, Regularization and Data Augmentation were applied to increase the model accuracy. The suggested model achieved an Accuracy of 72.28%, a Sensitivity of 71.12% and a Specificity of 93.1% for a testing dataset of 267 retinal fundus images from Kaggle and Messidor-2 datasets. By comparing with four pre-trained CNN models VGG16, ResNet50, InceptionV3 and Xception, it was observed that the accuracy of the suggested model is slightly lesser than that of VGG16 and ResNet50 models. However, the number of FLOPs in the suggested model is 23 times lesser than VGG16 and 6 times lesser than ResNet50, indicating that the suggested model is more efficient than the mentioned pre-trained models. The accuracy of the suggested model can be further improved without increasing the number of FLOPs by increasing the number of training data samples