Classification of Retinal Images Using Self-Created Penta-Convolutional Neural Network

Abstract

The primary way to classify retinal illnesses is to conduct several medical examinations, the most important of which is a visual examination. Human error is common as a result of a poor-higher cognitive process, which is one of the major challenges in visual disease diagnosis. Automated image processing technologies are more useful for early disease diagnosis and evaluation than the digitized diagnostic imaging conventional operations are confusing and time-consuming. The aim of this paper is to create a system that detects retinal abnormalities based on images using Deep learning technique. The images are first pre-processed. The photographs are enhanced after they have been pre-processed. The images that have been pre-processed are fed into the Penta-Convolutional Neural Network (Penta-CNN). Penta-CNN is a five-layered architecture that includes two convolutions, max pooling, and three fully connected layers. The performance of Penta-CNN is evaluated using STARE(Structured Analysis of the Retina) database [14]. The model is also trained with several hyperparameters which are tweaked and assessed.