A Systematic Review on Fundus Image-Based Diabetic Retinopathy Detection and Grading: Current Status and Future Directions

Abstract

Diabetic Retinopathy (DR) is a prevalent outcome of diabetic mellitus. This causes lesions to form on the retina, impairing eyesight. Most likely, blindness can be avoided if the DR condition is discovered at an initial stage. Since DR is a non-reversible condition, early detection and treatment can significantly reduce the chance of visual loss. Fundus images manually detect DR, which is a laborious and error-prone procedure. In assessing and categorizing medical images, machine learning and deep learning have emerged as the most efficient methods, surpassing human performance, common image processing methods, and other computer-aided detection systems. For this article, the most recent approaches for utilizing fundus images to classify and detect DR using machine learning and deep learning methods have been researched and evaluated. The freely accessible DR Datasets consisting of fundus images have also been discussed. We reviewed several DR pipeline components, including the datasets that researchers frequently used and the preprocessing and data augmentation steps, feature extraction methods, commonly used detection and classification algorithms, and the generally used performance metrics. This paper ends with a discussion of current challenges that have to be tackled by researchers working in this field to translate the research methodology into actual clinical practice. Finally, we conclude with a discussion of the future perspectives of DR.

Keywords

• Diabetic retinopathy
• machine learning
• fundus images
• computer-aided diagnosis
• retinal diseases