HYBRID DEEP SPIKING NEURAL PRINCIPAL COMPONENT ANALYSIS NETWORK FOR DIABETIC RETINOPATHY DETECTION USING RETINAL FUNDUS IMAGE

Diabetic Retinopathy (DR) is a common retinal vascular disease to injure the retinal blood vessels. DR causes the vision-related disorder without showing any symptoms. As the condition becomes more severe, it can result the partial or complete vision loss. Numerous clinical approaches were developed for treating DR; still, the processing cost and processing period are high. For solving such difficulties, the Deep Spiking Neural Principle Component Analysis Network (DSNPCANet) is proposed for DR detection. The input retinal fundus images are employed for detecting the DR. Pre-processing is the initial process, where the Wiener filter is utilized for eliminating the noise. The optic disc segmentation is used to segment the optic disc, where the active contour model is employed, moreover, the Artery/Vein Classification Network (AVNet) is employed for segmenting the blood vessel using the preprocessed images. Furthermore, the significant features are extracted from the preprocessed, optic disc-segmented, and blood vessel-segmented images. At last, the DSNPCANet is employed for DR detection. Moreover, the accuracy, sensitivity, specificity, precision, and $F$1-score are utilized to validate the DSNPCANet, which yields the finest values of 90.67%, 91.26%, 89.86%, 90.19%, and 90.14%.