Hybrid Deep Transfer Learning and Feature Fusion Architecture for Diabetic Retinopathy Classification and Severity Grading

Abstract

Diabetic Retinopathy (DR) is the leading cause of blindness among individuals with diabetes. Automating the diagnosis of DR has the potential to greatly benefit patients by enabling early detection and intervention, thus reducing the risk of blindness. The primary objective of this research is to develop a robust approach for the classification of DR and to analyze its severity grading. By achieving this, we aim to provide an effective tool for accurate diagnosis and assessment of DR, contributing to improved patient care and outcomes. The current literature review analysis reported the importance of deep learning in computer vision based applications. Moreover, plenty of pre-trained models are also present which can be used for classification tasks. Therefore, we present a hybrid DL classification approach by combining Inception V3, VGG-19 and ResNet 50. The proposed architecture uses transfer learning, and feature fusion model to produce the weighted feature vector which is used for classification analysis. The proposed approach is experimented on publicly available datasets APTOS-2019 and Messidor. The performance is measured in terms of accuracy, precision, recall and F1-score.