Studies show that at least 2.2 billion people in the world have some kind of visual
impairment or blindness. The prevalence of conditions progressing into preventable …

The present study carries out an empirical evaluation and comparison of the seven most
recent deep Convolutional Neural Network (CNN) techniques (VGG19, DenseNet121 …

Early identification and ongoing monitoring of individuals with eye illnesses have been key
concerns in computer-assisted detection. In computer aid screens, detecting one or more …

The only way to prevent blindness from eye problems is by early detection and prompt
treatment. Although colour fundus photography (CFP) is useful for fundus inspection, there is …

Healthcare is one of the industries where artificial intelligence has recently experienced a
period of significant growth of health. Retinal imaging is used by ophthalmologists to identify …

The field of ophthalmology offers great promise for improving patient care and outcomes via
automated diagnosis of eye illnesses. Using the Squeeze-and-Excitation Network (SENet) …

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 abnormalities impact millions of people globally. Timely detection and treatment of
these abnormalities could prevent further progression, potentially saving countless …

The rapid development of artificial neural network techniques, especially convolutional
neural networks, encouraged the researchers to adapt such techniques in the medical …

Eye diseases are one of the serious health problems affecting human life. Cataract, diabetic
retinopathy and glaucoma eye diseases cause visual impairment and cause irreversible eye …