Deep Learning Approaches for Detecting Glaucoma

The crucial problem of early glaucoma identification is addressed in this work; this neurodegenerative disease is referred to as the "sneak thief of vision." Increased intraocular pressure and the possibility for blinding optic nerve damage led to glaucoma's difficult early detection. The current diagnostic procedures take a lot of time and resources because they depend on skilled ophthalmologists and expensive equipment. This work presents a novel strategy leveraging deep learning techniques to get beyond these constraints. The EfficientNet-b7 architecture is shown to be extremely accurate by assembling a private dataset of annotated fundus photos and using many deep learning models, including as EfficientNet, MobileNet, Inception, and GoogLeNet. The study also investigates blood vessel segmentation from retinal fundus images using U-net, with promising outcomes. This study offers the possibility of automated glaucoma detection, providing a promising replacement for current techniques, with the potential for more accessible and accurate diagnoses.