Date of Award


Document Type

Campus Access Thesis

Degree Name

Master of Science (MS)


Computer Science

First Advisor

Marc Pomplun

Second Advisor

Dan Simovici

Third Advisor

Elizabeth O'Neil


Diabetic Retinopathy (DR) is a leading cause of vision loss among people with diabetes and is mainly caused by damages to blood vessels in the retinal region of the eye. However, the good news is that it is preventable in 98% of the patients provided the symptoms are diagnosed at an early stage. What makes diagnosis challenging is that there are about 415 million people in the world with diabetes and over 30% of them are at risk of developing DR related vision complications. The current ophthalmology screening process is both expensive and time consuming, making it infeasible to screen all diabetes patients for DR. A key step in screening involves identifying visible deviations in the retinal images as compared to the images from a healthy individual's eye. There are clinically documented patterns like circular red spots, swelling of tissues, yellow material deposits and unusual blood vessels that are key to identifying occurrence of DR. This thesis presents Deep Learning (DL) models constructed using Google's Inception framework for recognizing these unusual patterns in retinal images. The performance of these models, image preprocessing techniques and the benefits of using Google Cloud Platform (GCP) are also discussed.


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