Russell Hardie, Ph.D.
Electrical and Computer Engineering
Diabetic retinopathy is a disease that affects the eyes of people with diabetes, and it can cause blindness. To diagnose diabetic retinopathy, ophthalmologists image the back surface of the inside of the eye, a process referred to as fundus photography. Ophthalmologists must then diagnose and grade the severity of diabetic retinopathy by analyzing details in the image, which can be difficult and time-consuming. Alternatively, due to the availability of labeled datasets containing fundus images with diabetic retinopathy, AI methods like deep learning can provide automated detection and grading algorithms. We show that the resolution of an image has a large effect on the accuracy of grading algorithms. So, we study several techniques to increase the accuracy of the algorithm by taking advantage of higher-resolution data, including using a region of interest as the input and applying an image transformation to make the circular fundus image square. While none of our proposed methods result in an increase in performance for grading diabetic retinopathy, the circle to square transformation results in an increase in accuracy and AUC for detection of diabetic retinopathy. This work provides a useful starting point for future research aimed at increasing the resolution content in a fundus image.
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Saunders, Adam M., "Methods for Exploiting High-Resolution Imagery for Deep Learning-Based Diabetic Retinopathy Detection and Grading" (2023). Honors Theses. 423.