Ocular Disease Detection using Deep Convolution Neural Network

Srivastava, Priyanshu (2023) Ocular Disease Detection using Deep Convolution Neural Network. Masters thesis, Dublin, National College of Ireland.

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Abstract

Ophthalmic diagnostics are on the verge of a revolutionary development in a world where medical progress is constantly redefining the limits of healthcare. It holds the possibility of preserving vision and enhancing lives to be able to identify retinal disorders precisely and quickly. This study introduces a novel hybrid model that combines capsule networks with convolutional neural networks (CNNs) for better categorization of retinal diseases. The model uses CNNs for multi-label disease classification and capsule networks for hierarchical feature extraction from retinal pictures, all with the goal of improving ophthalmic diagnostics. The strategy addresses difficulties in detecting rarer illnesses like TSLN and ODC while demonstrating promising accuracy for big diseases like Diabetic Retinopathy (DR) and NORMAL instances. The model improves sensitivity and specificity for DR detection. This study highlights its potential as a useful tool for supporting medical professionals in the diagnosis of retinal diseases. To improve model performance and increase its clinical utility, future research will focus on data augmentation techniques, architectural improvements, and partnerships with experts in the field.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Rifai, Hicham UNSPECIFIED
Additional Information:	Retinal disease classification; Convolutional neural networks; Capsule networks; Automated diagnosis; Sensitivity; Specificity
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science R Medicine > RB Pathology R Medicine > RE Ophthalmology
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Tamara Malone
Date Deposited:	06 Jan 2025 17:48
Last Modified:	06 Jan 2025 17:48
URI:	https://norma.ncirl.ie/id/eprint/7276

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