Abstract:
Glaucoma is a chronic ocular condition that, if left untreated, can result in permanent blindness. Glaucoma
can be challenging to detect and diagnose due to its tendency to induce subtle alterations in the retina. Experienced
ophthalmologists frequently require extensive testing to determine the cause. This paper introduces a novel approach
to detect glaucoma automatically utilizing deep learning models, particularly a highly effective neural network. Our
methodology primarily utilizes retinal fundus images, a widely used imaging technique for assessing the condition of
the retina and optic nerve head. The work presents a specialized deep learning model designed to detect glaucoma,
which optimizes computer resources while maintaining high accuracy. The suggested neural network is designed to
efficiently analyze three-dimensional retinal images and acquire the ability to detect minor indications of glaucoma.
We employed data augmentation and improved image preprocessing techniques on a substantial collection of retinal
images to boost the practical utility of the model. This set contained both individuals without any health issues and
images depicting individuals at various stages of glaucoma. Our findings demonstrate that the model surpasses current
approaches in detecting glaucoma due to its superior accuracy, sensitivity, and specificity. The proposed model is
applicable in actual clinical environments due to its utility and efficacy. It provides ophthalmologists with a valuable
instrument for detecting and treating glaucoma at an early stage. This study further contributes to the existing
knowledge on utilizing deep learning techniques for the analysis of medical images. This demonstrates the application
of neural networks in enhancing healthcare results. The findings of this study have practical applications beyond the
detection of glaucoma. Additionally, they can assist in diagnosing other eye conditions that employ same deep learning
techniques.
