Open AccessA Review of Deep Learning Methods Applied to Ocular Diseases Recognition and Detection
Author(s) -
Prof. Vijayalakshmi,
. Mounesh,
L Vinay,
L Monisha,
Nithin MS
Publication year - 2022
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2022.41104
Subject(s) - macular degeneration , deep learning , diabetic retinopathy , optometry , medicine , fundus (uterus) , artificial intelligence , presbyopia , glaucoma , literacy , ophthalmology , segmentation , computer science , psychology , diabetes mellitus , pedagogy , endocrinology
Artificial intelligence is having an important effect on different areas of drug, and ophthalmology isn't the exception. In particular, deep literacy styles have been applied successfully to the discovery of clinical signs and the bracket of optical conditions. This represents a great eventuality to increase the number of people rightly diagnosed. ophthalmology, deep literacy styles have primarily been applied to eye fundus images and optic consonance tomography. On the one hand, these styles have achieved outstanding performance in the discovery of optical conditions similar as diabetic retinopathy, glaucoma, diabetic macular degeneration, and age- related macular degeneration. On the other hand, several worldwide challenges have participated big eye imaging datasets with the segmentation of part of the eyes, clinical signs, and optical judgments performed by experts. In addition, these styles are breaking the smirch of black-box models, with the delivery of interpretable clinical information. This review provides an overview of the state-of-the- art deep literacy styles used in ophthalmic images, databases, and implicit challenges for optical opinion. Keywords: Clinical signs; optical conditions; optical dataset; deep literacy; clinical opinion