In this work, deep learning algorithms are used to classify fundus images in terms of diabetic retinopathy severity. Six different combinations of two model architectures, the Dense Convolutional Network-121 and the Residual Neural Network-50 and three image types, RGB, Green, and High Contrast, were tested to find the highest performing combination. We achieved an average validation loss of 0.17 and a max validation accuracy of 85 percent. By testing out multiple combinations, certain combinations of parameters performed better than others, though minimal variance was found overall. Green filtration was shown to perform the poorest, while amplified contrast appeared to have a negligible effect in comparison to RGB analysis. ResNet50 proved to be less of a robust model as opposed to DenseNet121.
@article{arxiv.2108.08473,
title = {Classification of Diabetic Retinopathy Severity in Fundus Images with DenseNet121 and ResNet50},
author = {Jonathan Zhang and Bowen Xie and Xin Wu and Rahul Ram and David Liang},
journal= {arXiv preprint arXiv:2108.08473},
year = {2021}
}
Comments
15 pages, 14 figures; Jonathan Zhang - first author, Rahul Ram and David Liang - principal investigators; classifier repository - $\url{https://github.com/JZhang-305/Diabetic-Retinopathy-Classifier}$