Undergraduate Thesis

A Comparative Study of Diabetic Retinopathy Detection using Machine Learning and Deep Learning architectures.

Ailment of the eyes, Diabetic retinopathy is on the rise around the world, causing diabetics patients to lose their vision and become partially or completely blind. Diabetic retinopathy can be detected early, which can help patients to avoid losing their vision. This work offers a computer-aided diagnostic method based on digital retinal image processing and machine learning as well as deep learning models to help humans diagnose diabetic retinopathy early and compares their performance. Fundus images of both healthy and affected retina are taken in this study.

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