Abstract
Diabetic retinopathy is emerging as a very serious vision disorder in the recent decades, due to escalation of diabetes world-over. This condition can be minimized to a great extend with timely prognosis. Computer-aided detection techniques are very useful for assisting ophthalmologists, for faster diagnosis and intervention. With the advent of digital fundus cameras and the digitization of retinal images, there is a huge availability of digital fundus images with expert-annotated labels. For addressing the challenge of digital image grading, an attempt was made to model the features in digital fundus images, utilizing the non-Euclidean geometry. Here, a Graph Neural Network with supervised learning is suitably adapted for diabetic retinopathy image grading. The images are represented as 3D graphs, to encapsulate discriminate information, as nodes in network. The features extracted from the diabetic retinopathy images, using Scale Invariant Feature Transform technique, is used for graph construction and training. The Diabetic Retinopathy Graph Neural Network namely, DRG-NET model is trained and validated on two publicly available datasets namely Aptos 2019 and Messidor. Ten different types of performance indicators, including accuracy and Cohen’s kappa values, were estimated and used for the comparison of models. For the Aptos and Messidor dataset, the model achieved an accuracy of 0.9954/0.9984, F1-score of 0.9774/0.9968 and kappa score of 0.9930/0.9980, respectively. It is evident from the results that the proposed DRG-NET model shows state-of-the-art performance for retinal image grading.
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Salam, A.A., Mahadevappa, M., Das, A. et al. DRG-NET: A graph neural network for computer-aided grading of diabetic retinopathy. SIViP 16, 1869–1875 (2022). https://doi.org/10.1007/s11760-022-02146-x
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DOI: https://doi.org/10.1007/s11760-022-02146-x