Automated Diabetic Retinopathy Grading based on the Modified Capsule Network Architecture

The most significant source of eye disease is Diabetic Retinopathy (DR) among individuals who have had diabetes for a long time. Early or timely detection of DR stages is crucial for a better prognosis. The subtle distinction among different DR stages(level 0 to level 4) and many structures of varying shapes and sizes make manual recognition challenging and consume more time. Thus, this work focuses on the automatic grading of the retinal images (L-0 to L-4) based on the DR severity using a modified Capsule Network (Caps Net). The ability of Caps Net to capture spatial information of an object and the probability of some entity's existence in an image makes it more suitable for grading DR stages. This work proposes a hybrid model architecture that contains Caps Net followed by a Support Vector Machine(SVM) at its output layer. The main reason for incorporating an SVM at the output layer of Caps Net is to enhance SVM classification using automatic features extracted significantly but also alleviates the computational complexity of training and testing by the Caps Net model. The experiments were conducted on the APTOS 2019 Blindness Detection Data set. The investigation's outcome substantiates the proposed architecture's efficiency over the existing CNNs and Capsule Networks.