Deep feature extraction of retinal images for diabetic retinopathy enhanced diagnosis

Abstract

Most people of working age groups globally around are affected by diabetic retinopathy, which is the most common cause of preventable vision impairment. Recent studies have improved our comprehension of the need for more effective and affordable methods of identifying, managing, diagnosing, and treating retinal disease in clinical eye care practice. The development of computer-aided diagnosis tools must consider the significance of diabetic retinopathy screening programmes and the challenge of obtaining a valid early diagnosis of diabetic retinopathy at an affordable price. Retinal image analysis using computer-aided disease diagnosis may make it easier to screen the population for diabetes mellitus on a mass scale and may also assist physicians to make better use of their time. The creation of image processing models using big data analysis may run into many technical challenges, including the visualization analysis needed by image processing technologies, the semantic expression, and storage of large image samples, the complexity of the algorithms needed for feature extraction, recognition, and prediction analysis using big data, as well as the time and memory requirements.