Using Inception Network v3 to Grade Diabetic Retinopathy Images

Date of Award

Spring 2020

Degree Name

Bachelor of Science

Department

Computer Science & Mathematics; College of Arts & Sciences

First Advisor

Vitaly Ford

Abstract

Diabetic retinopathy is a disease that causes abnormalities in the blood vessels of the eye that can lead to blindness. Doctors often use a grading scale from 0-4 to describe the severity of the retinopathy, but grading can be somewhat inconsistent between doctors and in some areas of the world it can be difficult to get regular screenings. This project explores using Convolutional Neural Networks, or CNNs, to automate the process of grading retinal images in order to make care for diabetic patients faster, more consistent, and more accessible. CNNs are deep learning algorithms that are often used in imaging because they excel at identifying important features in images. The goal of this project is to train a type of CNN called an Inception Network to categorize retinal images using the 0-4 grading scale.

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Using Inception Network v3 to Grade Diabetic Retinopathy Images

Diabetic retinopathy is a disease that causes abnormalities in the blood vessels of the eye that can lead to blindness. Doctors often use a grading scale from 0-4 to describe the severity of the retinopathy, but grading can be somewhat inconsistent between doctors and in some areas of the world it can be difficult to get regular screenings. This project explores using Convolutional Neural Networks, or CNNs, to automate the process of grading retinal images in order to make care for diabetic patients faster, more consistent, and more accessible. CNNs are deep learning algorithms that are often used in imaging because they excel at identifying important features in images. The goal of this project is to train a type of CNN called an Inception Network to categorize retinal images using the 0-4 grading scale.