It is difficult for clinicians or less-experienced ophthalmologists to detect early eye-related
diseases. By hand, eye disease diagnosis is labor-intensive, prone to mistakes, and …

It can be challenging for doctors to identify eye disorders early enough using fundus
pictures. Diagnosing ocular illnesses by hand is time‐consuming, error‐prone, and …

Deep Learning (DL) has proliferated interest in ocular disease detection in recent years, and
several DL architectures were proposed. DL architectures deploy multiple layers to capture …

Purpose: The use of fundus images for the screening of different eye diseases is of
significant clinical importance. Early detection and diagnosis of ocular pathologies enable …

Retinal image analysis is an integral and fundamental step towards the identification and
classification of ocular diseases like glaucoma, diabetic retinopathy, macular edema, and …

Diabetic eye disease is a collection of ocular problems that affect patients with diabetes.
Thus, timely screening enhances the chances of timely treatment and prevents permanent …

Artificial intelligence (AI)-based diagnostic tools have been accepted in ophthalmology. The
use of retinal images, such as fundus photographs, is a promising approach for the …

The assistance of computer aided diagnostics will balance the insufficient number of
ophthalmologists burdened with an increasing number of patients suffering from Ocular …

The rapid development of computer vision technology and digital images has increased the
potential for using image recognition for eye disease diagnosis. Many early screening and …

Deep neural networks (DNNs) have been widely applied to the automatic analysis of
medical images for disease diagnosis and to help human experts by efficiently processing …