Date on Master's Thesis/Doctoral Dissertation


Document Type

Master's Thesis

Degree Name

M. Eng.



Degree Program

JB Speed School of Engineering

Committee Chair

El-Baz, Ayman

Committee Co-Chair (if applicable)

Elmaghraby, Adel

Committee Member

Elmaghraby, Adel

Committee Member

Giridharan, Guruprasad

Author's Keywords

diabetic retinopathy; deep learning; CNN; image classification; ultra-wide field imaging


Background: Diabetic retinopathy is a disease caused due by complications of diabetes mellitus which can lead to blindness. About 33% of the US population with diabetes also show symptoms for diabetes retinopathy. If not treated, diabetic retinopathy worsens over time by progressing through two main pathological stages of non-proliferative and proliferative and four clinical stages. While the diagnostic accuracy of detecting diabetic retinopathy through machine learning have shown to be successful for OCT images, the accuracy of ultra-widefield fundus images have yet to be fully reported. This paper describes a method to non-invasively detect and diagnose diabetic retinopathy from ultra-widefield fundus images.

Methods: A total of 62 graded-images were obtained from the Cleveland Clinic. A deep learning algorithm was developed to identify and extract features from the images. The algorithm was then simulated to classify the test images into one of three clinical classes. Data was collected on the accuracy and probability of the diagnosis/classification.

Results: The classification algorithm had an average accuracy that ranged from 92% to 97% for the training images and 50% for the test images. Confusion matrices were created to obtain statistical measures of performance such as sensitivity, false negative rate, precision, and the false discovery rate. The sensitivity decreased from 70% to 50% as the image size increased. The precision also decreased from 65% to 50% as the image size increased. Validation methods such as image normalization and transfer learning showed no improvement in classification accuracy.

Conclusion: This study demonstrates the potential for applying deep learning algorithms to classify ultra-widefield images. This study also demonstrates the need for doctors to further examine the diagnosis to account for false positives and/or misdiagnosis. Additionally, limitations and their impact on the simulation of the deep learning algorithm were explored.