Degree Name
MS (Master of Science)
Program
Computer Science
Date of Award
5-2024
Committee Chair or Co-Chairs
Bajracharya, Biju R
Committee Members
Brian Bennett, Mohammad Khan
Abstract
Healthcare analytics leverages extensive patient data for data-driven decision-making, enhancing patient care and results. Diabetic Retinopathy (DR), a complication of diabetes, stems from damage to the retina’s blood vessels. It can affect both type 1 and type 2 diabetes patients. Ophthalmologists employ retinal images for accurate DR diagnosis and severity assessment. Early detection is crucial for preserving vision and minimizing risks. In this context, we utilized a Kaggle dataset containing patient retinal images, employing Python’s versatile tools. Our research focuses on DR detection using deep learning techniques. We used a publicly available dataset to apply our proposed neural network and transfer learning models, classifying images into five DR stages. Python libraries like TensorFlow facilitate data preprocessing, model development, and evaluation. Rigorous cross-validation and hyperparameter tuning optimized model accuracy, demonstrating their effectiveness in early risk identification, personalized healthcare recommendations, and improving patient outcomes.
Document Type
Thesis - unrestricted
Recommended Citation
Olatunji, Aishat, "Detection and Classification of Diabetic Retinopathy using Deep Learning Models" (2024). Electronic Theses and Dissertations. Paper 4333. https://dc.etsu.edu/etd/4333
Copyright
Copyright by the authors.