A Fuzzy clustering Approach for Micro Aneurysm Lesion Detection in Diabetic Retinopathy

Abstract

Diabetic retinopathy (DR) be the significant difficulty of diabetes, and micro aneurysm (MA) is an earliest diabetic retinopathy lesion, making early detection of MA a key factor in diabetic retinopathy. DR is a direct or indirect effect on human vision caused by chronic diabetes. During its early stages DR is asymptomatic, and the late diagnosis leads to undeviating vision loss. The computer-assisted diagnosis helps with prompt and effective care, with the aid of medical photos. MA mark the beginning of DR making it a vital screening stage for this disorder. Diabetic retinopathy is a persistent infection of eye that can be the reason of blindness unless it is diagnosed and treated in due course. Early discovery with analysis of diabetic retinopathy is vital to vision preservation of patient. Precise recognition of MA be the crucial method towards early diagnosis of DR, since they occur as the first symptom of the disease. The segmentation of MA is performed using the Fuzzy C algorithm, and the extraction of features is performed with Gray Level Co-occurrence Matrix ( GLCM) as the set of characteristic for KNN. This technique aims to improve classification accuracy within an ensemble. A procedure is suggested here that recognizes the first DR sign called MA using images from the retinal fundus. Effective diagnosis of DR is very critical in the defense of patients' right to see. The procedure proposed is tested using publicly available databases of retinal images and greater accuracy is achieved.