Automated Retinal Hard Exudate Detection Using Novel Rhombus Multilevel Segmentation Algorithm

Abstract

Diabetic retinopathy causes blindness in diabetics. Early identification and frequent screening of diabetic retinopathy can slow disease progression and visual loss. Retinal lesions result from diabetic retinopathy. Dark and brilliant retinal lesions predominate. Color, shape, and size distinguish lesions. Exudates are bright, while microaneurysms (MAs) and hemorrhages (HEMs) are dark. This study presents a retinal lesion screening method for diabetic retinopathy. The data is saturated at low and high intensities; picture intensity values are adjusted to enhance contrast. This study presents a unique rhombus multilevel retinal image segmentation method. In the proposed study, preprocessing, segmentation algorithms, morphological operation,median filter and gradient are all designed as parts of an effective automated system. With 40 photos, the recommended method produced accuracy and specificity of 99.9% and 99.5%, respectively.