Graduation Year

2026

Document Type

Thesis

Degree

M.S.C.S.

Degree Name

MS in Computer Science (M.S.C.S.)

Degree Granting Department

Computer Science and Engineering

Major Professor

Lawrence Hall, Ph.D.

Committee Member

Dmitry Goldgof, Ph.D.

Committee Member

John Templeton, Ph.D.

Keywords

Classification, COVID, Image Reconstruction, Pneumonia, X-Ray

Abstract

Artificial neural networks trained to classify X-ray images according to disease state will learn to distinguish between technical or procedural variations in the images rather than features relevant to the disease. For example, the model might learn to recognize the machine that captured the image or to distinguish an image taken while the patient is lying supine or standing upright. The present work is aimed at using autoencoders as a generalizable classifier to detect COVID vs pneumonia (PNA) X-ray images. The first experiment was to find an architecture for a fully convolutional autoencoder (CAE) and for a convolutional autoencoder with a dense, fully connected middle layer (CDAE) that reconstructed the input images with as low an error as possible. First, several different CAEs were trained on a data set consisting of one source of COVID X-rays and one source of PNA X-rays. The best performing CAEs were then given a dense, fully connected inner layer and trained on the same data set. The CAE/CDAE pair with the least overall loss were chosen to be used in the following experiment. The second experiment was to use a pair of autoencoders with the same architecture as a classifier. One was trained on a single source of COVID images and the second on a single source of PNA images. The COVID and PNA images from sources other than the ones used for training comprised the test set. If the reconstruction loss for the COVID-trained autoencoder was less than that for the PNA-trained autoencoder, the image was classified as COVID and vice versa. Classifiers tested on images from the same source as the training set can achieve a perfect AUC of 1.0, but only have an AUC of 0.38 when tested on unseen sources [1]. In this research the best performing pair of CAEs achieved an overall accuracy of 62.5% with class-level F1-scores of 0.61 for COVID and 0.64 for PNA, and the best performing pair of CDAEs achieved an overall accuracy of 68.7% with class-level F1-scores 0.55 for COVID and 0.76 for PNA.

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