Application of Deep Learning in Fundus Image Processing for Ophthalmic Diagnosis -- A Review

TL;DR

This review paper summarizes how deep learning techniques are applied to analyze retinal fundus images for diagnosing various eye diseases, highlighting datasets, segmentation, detection, and classification methods.

Contribution

It provides a comprehensive overview of recent deep learning applications in ophthalmic diagnosis using fundus images, including datasets, segmentation, detection, and classification approaches.

Findings

Deep learning models effectively segment optic disk, blood vessels, and retinal layers.

Deep learning enables accurate classification of diseases like glaucoma and diabetic retinopathy.

Various datasets support deep learning research in ophthalmology.

Abstract

An overview of the applications of deep learning in ophthalmic diagnosis using retinal fundus images is presented. We also review various retinal image datasets that can be used for deep learning purposes. Applications of deep learning for segmentation of optic disk, blood vessels and retinal layer as well as detection of lesions are reviewed. Recent deep learning models for classification of diseases such as age-related macular degeneration, glaucoma,diabetic macular edema and diabetic retinopathy are also reported.