Optimal feature selection-based diabetic retinopathy detection using improved rider optimization algorithm enabled with deep learning

This proposal tempts to develop automated DR detection by analyzing the retinal abnormalities like hard exudates, haemorrhages, Microaneurysm, and soft exudates. The main processing phases of the developed DR detection model is Pre-processing, Optic Disk removal, Blood vessel removal, Segmentation of abnormalities, Feature extraction, Optimal feature selection, and Classification. At first, the pre-processing of the input retinal image is done by Contrast Limited Adaptive Histogram Equalization. The next phase performs the optic disc removal, which is carried out by open-close watershed transformation. Further, the Grey Level thresholding is done for segmenting the blood vessels and its removal. Once the optic disk and blood vessels are removed, segmentation of abnormalities is done by Top hat transformation and Gabor filtering. Further, the feature extraction phase is started, which tends to extract four sets of features like Local Binary Pattern, Texture Energy Measurement, Shanon's and Kapur's entropy. Since the length of the feature vector seems to be long, the feature selection process is done, which selects the unique features with less correlation. Moreover, the Deep Belief Network (DBN)-based classification algorithm performs the categorization of images into four classes normal, earlier, moderate, or severe stages. The optimal feature selection is done by the improved meta-heuristic algorithm called Modified Gear and Steering-based Rider Optimization Algorithm (MGS-ROA), and the same algorithm updates the weight in DBN. Finally, the effectual performance and comparative analysis prove the stable and reliable performance of the proposed model over existing models. The performance of the proposed model is compared with the existing classifiers, such as, NN, KNN, SVM, DBN and the conventional Heuristic-Based DBNs, such as PSO-DBN, GWO-DBN, WOA-DBN, and ROA-DBN for the evaluation metrics, accuracy, sensitivity, specificity, precision, FPR, FNR, NPV, FDR, F1 score, and MC. From the results, it is exposed that the accuracy of the proposed MGS-ROA-DBN is 30.1% higher than NN, 32.2% higher than KNN, and 17.1% higher than SVM and DBN. Similarly, the accuracy of the developed MGS-ROA-DBN is 13.8% superior to PSO, 5.1% superior to GWO, 10.8% superior to WOA, and 2.5% superior to ROA.