Edge Alterations as Predictive Biomarkers in Diabetic Retinopathy: A Deep Learning Approach

Bhave, Purvesh Lalit (2023) Edge Alterations as Predictive Biomarkers in Diabetic Retinopathy: A Deep Learning Approach. Masters thesis, Dublin, National College of Ireland.

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Abstract

Diabetic Retinopathy (DR), is a serious complication of diabetes mellitus and poses a major threat to the vision of a patient. The early detection and accurate prognosis of DR severity plays a vital role in effective management and treatment. This research presents a novel deep learning-based method to assess the correlation between DR severity levels and changes in retinal edges. Using a dataset of fundus images, a convolutional neural network (CNN) was used that focuses on the edge features within the retina. Some of these features include variations in vascular structure, microaneurysms, and hemorrhages, these features are critical indicators for DR progression. The implemented model was trained to classify images into multiple DR severity levels based on the International Clinical Diabetes Retinopathy scale. To validate the accuracy of the model, comparative analysis is done with another neural network model. Both models are evaluated based on an evaluation matrix containing precision, recall, and f1 score. This model adds to the expanding field of research on artificial intelligence in healthcare by emphasizing the true power of deep learning in improving diabetic retinopathy diagnosis. The techniques that are available at the moment are precise but expensive and need skilled personnel. The clinical significance of this research is to try to improve the management of Diabetic retinopathy

Item Type:	Thesis (Masters)
Supervisors:	Name Email Anant, Aaloka UNSPECIFIED
Uncontrolled Keywords:	Diabetic Retinopathy (DR); Deep Learning; CNN; Fundus; Edge Alteration; Hemorrhages; Microaneurysms
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine R Medicine > RE Ophthalmology Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	07 May 2025 11:31
Last Modified:	07 May 2025 11:31
URI:	https://norma.ncirl.ie/id/eprint/7500

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