DETECTION OF DIABETIC RETINOPATHY FROM RETINAL FUNDUS IMAGES USING IMAGE SEGMENTAION AND AUTOMATIC DEEP LEARNING MODEL

Early diagnosis and treatment of diabetic retinopathy (DR) are critical to minimize or avoid vision loss. Furthermore, automatically locating the areas of the retinal image that may have lesions can help experts in the detection process. This paper proposes a deep learning technique with an adequate image segmentation mechanism for recognizing DR from retinal fundus data. Initially, the fundus data is collected from the openly available dataset. Then, the Fast K-Nearest Neighbour Image Segmentation Module (FKISM) is developed to segment the retinal lesions from the collected fundus data. Then, Auto Feature Selection Convolution Neural Network (AFSCNN) is applied to perform automated feature, earning and classification of DR. Simulations are done to analyze the proposed system’s effectiveness with the existing methodologies regarding classification metrics, and our proposed method illustrates superior performance by attaining higher outcomes in both training and testing contrasted to the traditional methods.