Dataset-Driven Deep Learning Methods for Diabetic Retinopathy Analysis

Abstract

Diabetic retinopathy (DR) is a leading cause of blindness globally, necessitating early detection and precise grading for effective management and treatment. This review paper synthesizes recent advancements in automated DR detection and grading methodologies, emphasizing machine learning and deep learning approaches. This review paper provides a comprehensive analysis of recent advancements in the detection and grading of diabetic retinopathy (DR) using various machine learning and deep learning methods across multiple datasets. The analysis includes studies that employed a wide range of techniques such as principal component analysis (PCA), transfer learning, Vision Transformers, multi-stage deep convolutional neural networks (CNNs), and hybrid approaches combining CNN with singular value decomposition (SVD). Each method was evaluated on different datasets, including Messidor, E-Ophtha, Kaggle Diabetic Retinopathy, APTOS 2019 Blindness Detection, Indian Diabetic Retinopathy Image Dataset (IDRiD), EyePACS, and OCTID. The results consistently demonstrated high accuracy, precision, recall, and F1 scores, with most methods achieving accuracy rates above 90%, indicating their effectiveness in clinical applications. Notably, transfer learning approaches, such as those incorporating Error-Correcting Output Codes (ECOC) and context encoders, showed superior performance, particularly on larger and more diverse datasets. This paper highlights the robustness and potential of these advanced techniques in improving the early detection and management of diabetic retinopathy, providing valuable insights for future research and development in this critical area of medical imaging.