Date of Award
Spring 2021
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Computer Science
Program
Computing
First Advisor
Ahamed, Sheikh I.
Second Advisor
Iqbal, Anik
Third Advisor
Sikder, Abdur
Abstract
Retinal eye disease is the most common reason for visual deterioration. Long-term management and follow-up are critical to detect the changes in symptoms. Optical Coherence Tomography (OCT) is a non-invasive diagnostic tool for diagnosing and managing various retinal eye diseases. With the increasing desire for OCT image, the clinicians are suffered from the burden of time on diagnoses and treatment. This thesis proposes an auto-grading diagnostic tool to divide the OCT image for retinal disease classification. In the tool, the classification model implements convolutional neural networks (CNNs), and the model training is based on denoised OCT images. The tool can detect the uploaded OCT image and automatically generate a result of classification in the categories of Choroidal neovascularization (CNV), Diabetic macular edema (DME), Multiple drusen, and Normal. The system will definitely improve the performance of retinal eye disease diagnosis and alleviate the burden on the medical system.