Multi-Class Eye Disorder Classification using CNN

-, Shahrukh (2024) Multi-Class Eye Disorder Classification using CNN. Masters thesis, Dublin, National College of Ireland.

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Abstract

The sense of vision in humans is critical, and the eyes are very important in everyday life. Different conditions of eye disorder could cause blindness, like, as cataract disease, the disease AMD also known as Age-Related Mascular Degeneration, Diabetic Retinopathy (DR) disease, pathological myopia, and glaucoma disorder, these all could damage the visual system leading to blindness. Early diagnosis and accurate detection are crucial to prevent the loss of sight. Traditional ways of diagnosing them are time-consuming with high error rates hence there is a need for the development of better diagnostic tools. This research considers the implementation of deep learning specifically CNN that can help enhance accuracy and speed in ocular disease diagnosis using fundus images. The paper seeks to review existing methodologies critically, construct the CNN model, employ data preprocessing and augmentation approaches as well as assess their influence on model performance to make multiple eye disease diagnoses more accurate and faster resulting in improved patient outcomes. After the preprocessing and data augmentation, several experiments are performed to increase the accuracy of disease prediction. The final approach achieved an accuracy of 81% for the multi-class disease classification for ocular disorders.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Horn, Christian UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science R Medicine > RE Ophthalmology Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning H Social Sciences > HM Sociology > Information Science > Communication > Medical Informatics
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	06 Aug 2025 14:32
Last Modified:	06 Aug 2025 14:32
URI:	https://norma.ncirl.ie/id/eprint/8447

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