Application of Deep Learning in Fundus Image Processing for Ophthalmic Diagnosis -- A Review
It provides a comprehensive overview for researchers and practitioners in medical imaging and ophthalmology, but is incremental as it synthesizes existing work without introducing new methods.
This review paper surveys the application of deep learning techniques in ophthalmic diagnosis using retinal fundus images, covering segmentation tasks like optic disk and blood vessel detection, as well as disease classification for conditions such as age-related macular degeneration and diabetic retinopathy.
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.