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By: Anshul Jain and Vikas Sakalle.
1- Research Scholar, Department of Computer Science and Engineering, Lakshmi Narain College of Technology (LNCT) University, Bhopal, Madhya Pradesh, India
2- Associate Professor, Department of Computer Science and Engineering Lakshmi Narain College of Technology (LNCT) University, Bhopal, Madhya Pradesh, India
One of the main causes of irreversible blindness is glaucoma, which is characterized by certain alterations in the optic nerve that over time can impair vision. Manual evaluations, while invaluable, are challenged by inconsistencies, subjectivity, and the extensive time required. The study employed a dataset comprising retinal images annotated by experts. By using Convolutional Neural Networks (CNNs) to study and recognize important patterns in eye images, it was possible to accurately distinguish between optic nerves that were healthy and those that were damaged by glaucoma. To locate and classify glaucoma optic disks, this paper explores the emerging field of machine learning and deep learning models. We explore the methods, discoveries, and advancements that have influenced the field of automated glaucoma detection as we progress through the current research environment. The findings suggest that deep learning models can play a crucial role in the automated detection of glaucoma, supporting ophthalmologists in clinical decision-making and reducing the burden of manual diagnosis. Further research and clinical validation are recommended to refine these models and integrate them into routine clinical practice.
Keywords: Glaucoma, optic disk, machine learning, convolutional neural networks, diagnosis,
ResNet 101, and ResNet 152, RIM-ONE v3
Citation:
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