Automatic detection of hypertensive retinopathy using improved fuzzy clustering and novel loss function

Hypertension retinopathy is a retinal disease caused due to hypertension which leads to vision loss and blindness. Ophthalmologists use clinical methods to perform the diagnosis, which takes more time and money. Still, the computer-aided diagnostic system detects and grades Hypertensive Retinopathy with no time and is less expensive. This paper introduces an automated system that identifies hypertension retinopathy in the early stage of hypertension. Retinal image segmentation efficiently detects eye ailments, which are the signs of major eye diseases caused by hypertension, diabetes, and age-related macular disorders. This study uses fuzzy logic techniques in digital image processing and mainly concentrates on the early detection of hypertension retinopathy by using a nature-inspired optimization algorithm. Improved Fuzzy C-Means clustering identifies the lesion regions in hypertensive retinopathy accurately. The present model is tested on the publicly available online dataset, and its outcomes are compared with distinguished published methods. This study calculates the segmented output on the optimized features using the improved loss function in the Resnet-152 model. The proposed approach improves performance and surpasses the existing state-of-the-art models.