Particle Swarm Optimization based Detection of Diabetic Retinopathy using a Novel Deep CNN

Diabetic Retinopathy (DR) is a disease. Diabetic patients are mostly affected by this disease. This causes damage to the eyes. Most probably the disease affects both the back of the eye and the blood vessels of the light-sensitive. Initially, this DR disease causes only minor problems with vision. But eventually, the eyesight will go away. This condition may occur in any diabetic patient. When the disease is more affected, the blood sugar level is high. DR detection is the application of image processing to medical examinations. To diagnose DR the retinal images should be evaluated. However, it takes a lot of time and resources to physically assess the images so that the severity of the DR can be removed. The blood vessels in the retina are damaged due to this problem can be noticed. When it starts to bleed the small vessels, then it forms a way on the retina. To overcome the challenges of DR, CNN with PSO is introduced. There are different stages implicated in the diabetic retinopathy detection technique. They are pre-processing, optimization and classification. The main objective of the proposed work is to use the CNN algorithm to analyze the disease that seems to be most affected and classify and report only that area from the given input. PSO is used to optimize the given classified images, and then finally PSO with CNN technique will produce accurate results.