Deep learning-based methods have become the dominant methodology in medical image
processing with the advancement of deep learning in natural image classification, detection …

Detecting multiple ocular diseases in fundus images is crucial in ophthalmic diagnosis. This
study introduces the Fundus-DeepNet system, an automated multi-label deep learning …

Early detection of eye diseases is the only solution to receive timely treatment and prevent
blindness. Colour fundus photography (CFP) is an effective fundus examination technique …

In today's world, ocular diseases have become widespread. The “ocular illness” refers to any
abnormality, impairment, or dysfunction of vision that impacts the eye. The most common …

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 …

Primary care doctors have been fighting against ocular illnesses for more than 37% of the
world's population. This demonstrates the need for an autonomous and intelligent …

Deep Learning (DL) plays a successful and influential role in medical imaging diagnosis,
image detection, and image classification. Diabetes is a significant public health concern …

The rapid integration of Artificial Intelligence (AI) has significantly improved healthcare
outcomes, especially in ophthalmology. However, Deep learning algorithms are often called …

This paper addresses the efficacy of conducting early fundus screenings to mitigate the risk
of vision loss from ophthalmic diseases and presents a solution utilizing deep learning for …

This study aims to create a machine learning-based method for categorizing ocular
impairment. Congenital, refractive error, age, diabetes, and unknown are the five primary …