In response to the evolving landscape of healthcare technology this study addresses the
need for automated diagnostic tools in the field of ophthalmology. By utilizing deep learning …

The objective of this research is to examine the utilization of deep learning in the
classification of ocular diseases and healthy lives, with a specific emphasis on the …

This paper searches into the convergence of such ad-vanced techniques with architectures
like DenseNet201, VGG16, InceptionResNetV2, and NasNetMobile. Our focus centers on …

Ocular diseases present a significant public health concern globally, warranting precise and
efficient diagnostic methodologies. This research aims to develop an approach for …

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) …

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 …

Ophthalmic disorders represent a major global health problem leading to visual impairment
and even blindness if not detected and treated early. Deep learning is a popular approach …

Medical image classification plays a vital part in identifying and detecting diseases. Vision
impairment affects 2.2 billion individuals globally, with cataracts, glaucoma, and diabetic …

Currently millions of individuals worldwide are suffering from ocular diseases. Diagnosis of
ocular diseases by conventional methods is challenging, labor-intensive and prone to …

The present study aims to investigate the crucial topic of automated categorization of eye
diseases using medical photographs by utilizing the capabilities of the ResNet-18 model …