A Comprehensive Review on Diabetic Retinopathy Detection Techniques using Neural Network Architectures

Abstract

Diabetic retinopathy (DR) is a significant complication arising from diabetes, affecting the eyes and potentially causing vision loss if not identified and addressed promptly. Over the years, there has been a significant advancement in the field of DR detection, primarily driven by advancements in imaging techniques and machine learning algorithms. This review paper presents a comprehensive overview of different techniques and advancements in the detection of diabetic retinopathy using deep learning and several neural network architectures. The comparative study of the existing datasets for the DR detection with the benefits, challenges and possible solutions for each dataset is also provided. The paper discusses the methods, preprocessing, implementation platforms and results of various implementation of CNN architectures like Deep CNN, CNN with Transfer Learning, Capsule Networks and DNN. The objective of this paper is to furnish researchers and clinicians with a thorough understanding of the present status of diabetic retinopathy detection, highlight the strengths and limitations of existing approaches, and identify future research directions in this vital area of healthcare.