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

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.

References in corpus (15)

• Adam: A Method for Stochastic Optimization
• Deep Visual Domain Adaptation: A Survey
• Retinal Vessel Segmentation Using the 2-D Morlet Wavelet and Supervised Classification
• Deep image mining for diabetic retinopathy screening
• Optic Disc and Cup Segmentation Methods for Glaucoma Detection with Modification of U-Net Convolutional Neural Network
• Disc-aware Ensemble Network for Glaucoma Screening from Fundus Image
• Retinal vessel segmentation based on Fully Convolutional Neural Networks
• An ensemble-based system for automatic screening of diabetic retinopathy
• Improved Microaneurysm Detection using Deep Neural Networks
• Ensemble of Deep Convolutional Neural Networks for Learning to Detect Retinal Vessels in Fundus Images
• Towards Adversarial Retinal Image Synthesis
• PixelBNN: Augmenting the PixelCNN with batch normalization and the presentation of a fast architecture for retinal vessel segmentation
• Embedded deep learning in ophthalmology: Making ophthalmic imaging smarter
• Classification of Findings with Localized Lesions in Fundoscopic Images using a Regionally Guided CNN
• A Deep Learning based Joint Segmentation and Classification Framework for Glaucoma Assesment in Retinal Color Fundus Images

Cited by in corpus (7)

• Medical Deep Learning -- A systematic Meta-Review
• A Foundation Language-Image Model of the Retina (FLAIR): Encoding Expert Knowledge in Text Supervision
• A Benchmark of Ocular Disease Intelligent Recognition: One Shot for Multi-disease Detection
• Rapid Classification of Glaucomatous Fundus Images
• Applications of Deep Learning in Fundus Images: A Review
• Quantitative and Qualitative Evaluation of Explainable Deep Learning Methods for Ophthalmic Diagnosis
• Multi-modal, multi-task, multi-attention (M3) deep learning detection of reticular pseudodrusen: towards automated and accessible classification of age-related macular degeneration