RDD-Net: retinal disease diagnosis network: a computer-aided diagnosis technique using graph learning and feature descriptors

Amritha Abdul Salam, Manjunatha Mahadevappa, Asha Das, Madhu S. Nair

Abstract: Ocular diseases are a prevalent disease among the aging population across the world. The retinal damage and vision loss can be substantially decreased through early-stage diagnosis with computer-aided ocular disease diagnosis. With the use of color fundus photography for obtaining digital retinal fundus images, there is a growth in the online accessibility of digital fundus images. For diagnosing ocular diseases, an attempt was made to model graphs from images for feature learning. Three feature detection algorithms, namely scale-invariant feature transform, binary robust invariant scalable keypoints and oriented fast and rotated BRIEF (ORB) techniques are computed individually. As graphs are represented in the non-Euclidean domain, the graph neural network is used to learn the node embedding to model the network for ocular disease diagnosis. Three distance measures: the Euclidean, Manhattan and Chebyshev distances, are computed for analyzing the discriminative power of the model. The proposed RDD-Net model is trained and evaluated on the ODIR-2019 dataset with eleven different performance indicators. The results show that mapping images to non-Euclidean geometric space have obtained a successful diagnosis of ocular diseases from digital fundus images. The ORB descriptor outperforms the other two feature descriptors as well as the existing algorithms for ocular disease diagnosis. Results of the Chebyshev distance measure show superior performance when compared to the other two distance measures based on computation time and performance evaluation metrics. The proposed RDD-Net achieves an F1-score of 0.9970 and a sensitivity of 0.9969 with the ORB descriptor and shows state-of-the-art performance.