Authors: Santosh Nagnath Randive; Ranjan K. Senapati; Amol D. Rahulkar
Addresses: Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram, Guntur, Andhra Pradesh 522502, India ' Department of Electronics and Communication Engineering, VNR Vignana Jyothi Institute of Engineering and Technology, Vignana Jyothi Nagar, Nizampet Rd., Pragathi Nagar, Telangana, India ' Department of Electrical and Electronics Engineering, National Institute of Technology Goa, Goa, India
Abstract: Diabetic retinopathy (DR) is a health issue which curtailed from diabetes for over long periods. When symptoms are rigorous, the patient might be blinded. The only way to avoid this problem is screening by an ophthalmologist. Anyhow, the automated detection utilises more time, and in addition, it includes several experts. Hence, this paper intends to propose a new diagnosis model of DR, which determines the severity of retinopathy. This model exploits three stages including segmentation, feature extraction, and classification. Here, density-based spatial clustering model (DBSCAN) is exploited for segmentation process, where it segments the abnormalities of retina. Coming to the feature extraction stage, this paper proceeds to extract the GLCM and GLRM features from the given input fundus image. Exclusively, it aims at a subjective contribution, i.e., the required features are optimally selected in this phase. Finally, for classification, this paper deploys a renowned neural network (NN) model. As the second contribution, the weight in NN model is optimally chosen. The optimal issues (selection of optimal features and weights) are successively solved by a new self-adaptive grey wolf optimisation (SA-GWO) approach. Moreover, the proposed method is evaluated with other conventional schemes, and the outcomes are obtained.
Keywords: diabetic retinopathy; feature extraction; classification; self-adaptive grey wolf optimisation; SA-GWO; segmentation; image processing; feature selection; neural network; optimised training.
International Journal of Nano and Biomaterials, 2019 Vol.8 No.3/4, pp.204 - 227
Received: 05 Jul 2018
Accepted: 23 Nov 2018
Published online: 06 Feb 2020 *