An Efficient Meta-Heuristic-Feature Fusion Model using Deep Neuro-Fuzzy Classifier

• Published in: International journal of advanced computer science & applications 2022-01, Vol.13 (11)
• Main Authors: Kuna, Sri Laxmi, Prasad, A. V. Krishna
• Format: Article
• Language: English
• Subjects: Abnormalities; Algorithms; Artificial neural networks; Blood vessels; Classification; Classifiers; Clustering; Contouring; Diabetic retinopathy; Exudation; Fuzzy logic; Hemorrhage; Heuristic methods; Image enhancement; Image segmentation; Mathematical models; Optimization; Parameter modification; Retinal images
• ISSN: 2158-107X; 2156-5570
• DOI: 10.14569/IJACSA.2022.01311100

Diabetic Retinopathy (DR) is the major cause of the loss of vision among adults worldwide. DR patients generally do not have any symptoms till they reach the final stage. The categorization of retinal images is a remarkable application in detecting DR. Due to the level of sugar available in the blood, the categorization of DR severity becomes complicated to determine the grading level of the damages caused in the retina. To rectify these challenges, a new DR severity classification model is proposed for detecting and treating the DR. The main objective of the proposed model is to classify the severity grades that occurred in the retinal region of the human eye. Initially, gathered retinal images are enhanced and the blood vessel segmentations are done by utilizing the optic disc removal and active contouring model. The abnormalities such as “microaneurysms, hemorrhages, and exudates” are segmented by utilizing Fuzzy C-Means Clustering (FCM) and adaptive thresholding. Then, the segmented images are given to “VGG16 and ResNet”, in which the two different feature sets are acquired. Then, these features are added to obtain the second set of features as F2. Again, the enhanced images act as an input to the “VGG16 and ResNet”, which are attained as the first feature set as F1. In the feature concatenation phase, the resultant of two features is used for feature fusion with the aid of weights parameter that is optimized by Modified Mating Probability-based Water Strider Algorithm (MMP-WSA), where the feature fusion is carried out using the mathematical expression. Finally, the multi-class severity classifications are done by using the Optimized Deep Neuro-Fuzzy Classifier (ODNFC), where the optimization of hyper-parameters is done by the proposed MMP-WSA. Thus, the experimental results of the proposed model have been acquired by the precise segment of the abnormalities and better classification results regarding the grade level.