Hybrid deep learning approaches for the detection of diabetic retinopathy using optimized wavelet based model

Abstract

Diabetic retinopathy (DR) is a diabetic ocular manifestation that leads to loss of visual impairment and blindness in people worldwide. Detecting and diagnosing the DR remains a major question in this task. This developed work uses a robust hybrid binocular Siamese with a deep learning approach to classify the DR image. Initially, a pre-processing stage is introduced to remove unwanted noises. To perform this cross guided bilateral filter (CGBF) approach is emphasized. After the pre-processing, the feature extraction stage is presented to extract the features from the processed image. A wavelet-based Chimp optimization algorithm (WBCOA) is established for the extraction of features. After feature extraction, segmentation of optical disc (OD) and blood vessel (BV) is done via open closed watershed management (OCWSM). For the classification, binocular Siamese based AlexNet and GoogleNet with the SVM model are proposed in this work. The segmented OD and BV are input to the proposed hybrid DL network. Finally, the extracted images are fused and classified using the Support Vector Machine (SVM) model. The proposed method is implemented in Python and tested on DIARETDB0 (DB0) and DIARETDB1 (DB1) datasets. The proposed hybrid DL network attained 94% and 94.83% accuracy on DB0 and DB1 datasets, respectively. Also, the proposed model’s outcomes are compared with various existing approaches. The proposed method conducted statistical analysis for the DR image based on mean and standard deviation (SD) to obtain an efficient output. These outcomes prove that the proposed hybrid DL system is accurate for early DR detection and deliver effective treatment of diabetes.