A Coherent Ensemble Modeling Approach for Diabetic Retinopathy Using MIFNET Method

The primary reason for blindness in wealthy countries is diabetic retinopathy, a devastating eye condition that results from diabetes mellitus. This article demonstrates how retinal fundus pictures may be used to detect diabetic retinopathy using image processing and deep learning. A practical method that incorporated the techniques was employed for the improvement of retinal fundus pictures. Every step of the image processing process has been classified in experiments. The categorization analysis was carried out following picture processing. Average values were discovered after 20 trials were conducted for each level. In this trial, the recall rate was 93.33%. The outcomes demonstrate how effective and successful the suggested approach is at using retinal fundus pictures to identify diabetic retinopathy. We are segmenting the region depending on sensitivity specificity and accuracy using the MIFNET method, and we are training the CNN algorithm model utilizing DLT.