Multi-class diagnosis of diabetic retinopathy using deep learning

Abstract

Diabetic Retinopathy is the main cause of blindness in the modern world. As per studies, around 40-45% of people suffering from diabetes have DR in their later stages of life. All the forms of diabetic eye disease have the potential to cause vision impairment or blindness. Early stages of DR shows very small and intricate features like micro-aneurysms (swelling of blood vessels), hard exudates (protein deposits), whereas the severe and proliferative stages show more prominent features like hemorrhages (blood clot), neovascularization (abnormal growth of vessels), macular edema etc. Detecting such small and complex features through Fundus images is a very tedious and time-consuming process and requires an experienced ophthalmologist. This demands an automated diagnosis system which can vastly reduce the burden on the clinicians. In this thesis, we propose a Convolutional Neural Network (CNN) based automated diagnosis system that can classify various stages of diabetic retinopathy accurately. A hierarchical approach is adopted for classification in which we break down our classification task into two stages. In the first stage we perform binary classification and find out the true positive and negative samples and in the second stage, five class classification is performed with the images which were classified as true positive, false positive and false negative in the first stage of classification. Our proposed method uses the Inception-v3 net for feature extraction. Our proposed method uses the Inception-v3 net for feature extraction in which we use the features of second last layer features and also the features from the second last layer of an auxiliary classifier. These extracted features are concatenated into a single feature vector to train a Support Vector Machine (SVM). For multiclass classification, SVM classifies sample into one of the five classes. Experiments are conducted on "Kaggle" dataset and our proposed approach attains an accuracy of 91% on validation data for binary classification and 78% for multiclass classification. The results obtained are better than the recent methods on multiclass classification of diabetic retinopathy