Automatic Diagnosis of Diabetic Retinopathy from Retinal Abnormalities: Improved Jaya-Based Feature Selection and Recurrent Neural Network

Abstract

Abstract Accurate diagnosis of lesions bears the highest significance in the early detection of diabetic retinopathy (DR). In this paper, the combination of intelligent methods is developed for segmenting the abnormalities like ‘hard exudates, hemorrhages, microaneurysm and soft exudates’ to detect the DR. The proposed model involves seven main steps: (a) image pre-processing, (b) optic disk removal (c) blood vessel removal, (d) segmentation of abnormalities, (e) feature extraction, (f) optimal feature selection and (f) classification. The pre-processing of the input retinal fundus image is performed by two operations like contrast enhancement by histogram equalization and filtering by average filtering. For the segmentation of abnormalities, the same Circular Hough Transform followed by Top-hat filtering and Gabor filtering is used. Next, the entropy-scale-invariant feature transform (SIFT), grey level co-occurrence matrices and color morphological features are extracted in feature extraction. The optimally selected features are subjected to the classification part, which uses a modified deep learning algorithm called optimized recurrent neural network (RNN). As the main novelty, the optimal feature selection and optimized RNN depends on an improved meta-heuristic algorithm called fitness oriented improved Jaya algorithm. Hence, the beneficial part of the optimization algorithm improves the feature selection and classification.