Classification of eye-fundus images with diabetic retinopathy using shape based features integrated into a convolutional neural network

The paper proposes a CNN architecture in which features extracted using shape from eye-fundus images are fed as an input. Firstly, we preprocess the images extensively. These preprocessed images are thereby used in the extraction of shape based features. The feature set obtained is then fed into Convolutional neural network (CNN) architecture for classification of retinopathy images into different groups. The proposed feature set shows an improvement in Average Retrieval Rate (ARR) up to 19.88% and Average retrieval precision (ARP) up to 12.01% over the vgg-f architecture of deep networks used for classification.