Date of Award
8-1-2022
Degree Name
Master of Science
Department
Electrical and Computer Engineering
First Advisor
Chen, Ying
Abstract
This paper examines the use of convolutional neural networks to classify Covid-19 in chest radiographs. Three network architectures are compared: VGG16, ResNet-50, and DenseNet-121 along with preprocessing methods which include contrast limited adaptive histogram equalization and non-local means denoising. Chest radiographs from patients with healthy lungs, lung cancer, non-Covid pneumonia, tuberculosis, and Covid-19 were used for training and testing. Networks trained using radiographs that were preprocessed using contrast limited adaptive histogram equalization and non-local means denoising performed better than those trained on the original radiographs. DenseNet-121 performed slightly better in terms of accuracy, performance, and F1 score than all other networks but was not found to be statistically better performing than VGG16.
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