Evaluating Pre-training Mechanisms in Deep Learning Enabled Tuberculosis Diagnosis
| dc.contributor.author | Zaranyika, Zororo | |
| dc.contributor.supervisor | Klein, Richard | |
| dc.date.accessioned | 2024-11-05T10:45:21Z | |
| dc.date.available | 2024-11-05T10:45:21Z | |
| dc.date.issued | 2024 | |
| dc.description | A research report submitted to the Faculty of Science in partial fulfillment of the requirements for the degree of Master of Science by Course Work and Research Report in the field of Computer Science, University of the Witwatersrand, Johannesburg, 2024 | |
| dc.description.abstract | Tuberculosis (TB) is an infectious disease caused by a bacteria called Mycobacterium Tuberculosis. In 2021, 10.6 million people fell ill because of TB and about 1.5 million lives are lost from TB each year even though TB is a preventable and curable disease. The latest global trends in TB death cases are shown in 1.1. To ensure a higher survival rate and prevent further transmissions, it is important to carry out early diagnosis. One of the critical methods of TB diagnosis and detection is the use of posterior-anterior chest radiographs (CXR). The diagnosis of Tuberculosis and other chest-affecting dis- eases like Pneumoconiosis is time-consuming, challenging and requires experts to read and interpret chest X-ray images, especially in under-resourced areas. Various attempts have been made to perform the diagnosis using deep learning methods such as Convolutional Neural Networks (CNN) using labelled CXR images. Due to the nature of CXR images in maintaining a consistent structure and overlapping visual appearances across different chest-affecting diseases, it is reasonable to believe that visual features learned in one disease or geographic location may transfer to a new TB classificationmodel. This would allow us to leverage large volumes of labelled CXR images available online hence decreasing the data required to build a local model. This work will explore to what extent such pre-training and transfer learning is useful and whether it may help decrease the data required for a locally trained classifier. In this research, we investigated various pre-training regimes using selected online datasets to under- stand whether the performance of such models can be generalised towards building a TB computer-aided diagnosis system and also inform us on the nature and size of CXR datasets we should be collecting. Our experiment results indicated that both supervised and self-supervised pre-training between the CXR datasets cannot significantly improve the overall performance metrics of a TB. We noted that pre-training on the ChestX-ray14, CheXpert, and MIMIC-CXR datasets resulted in recall values of over 70% and specificity scores of at least 90%. There was a general decline in performance in our experiments when we pre-trained on one dataset and fine-tuned on a different dataset, hence our results were lower than baseline experiment results. We noted that ImageNet weights initialisation yields superior results over random weights initialisation on all ex- periment configurations. In the case of self-supervised pre-training, the model reached acceptable metrics with a minimum number of labels as low as 5% when we fine-tuned on the TBX11k dataset, although slightly lower in performance compared to the super-vised pre-trained models and the baseline results. The best-performing self-supervised pre-trained model with the least number of training labels was the MoCo-ResNet-50 model pre-trained on the VinDr-CXR and PadChest datasets. These model configura- tions achieved recall scores of 81.90% and a specificity score of 81.99% on VinDr-CXR pre-trained weights while the PadChest weights scored a recall of 70.29% and a speci- ficity of 70.22%. The other self-supervised pre-trained models failed to reach scores of at least 50% on both recall or specificity with the same number of labels | |
| dc.description.submitter | MM2024 | |
| dc.faculty | Faculty of Science | |
| dc.identifier.citation | Zaranyika, Zororo . (2024). Evaluating Pre-training Mechanisms in Deep Learning Enabled Tuberculosis Diagnosis [Master’s dissertation, University of the Witwatersrand, Johannesburg]. WireDSpace.https://hdl.handle.net/10539/42180 | |
| dc.identifier.uri | https://hdl.handle.net/10539/42180 | |
| dc.language.iso | en | |
| dc.publisher | University of the Witwatersrand, Johannesburg | |
| dc.rights | © 2024 University of the Witwatersrand, Johannesburg. All rights reserved. The copyright in this work vests in the University of the Witwatersrand, Johannesburg. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of University of the Witwatersrand, Johannesburg. | |
| dc.rights.holder | University of the Witwatersrand, Johannesburg | |
| dc.school | School of Computer Science and Applied Mathematics | |
| dc.subject | Tuberculosis (TB) | |
| dc.subject | UCTD | |
| dc.subject | Mycobacterium Tuberculosi | |
| dc.subject | TUBERCULOSIS DIAGNOSIS | |
| dc.subject.other | SDG-4: Quality education | |
| dc.title | Evaluating Pre-training Mechanisms in Deep Learning Enabled Tuberculosis Diagnosis | |
| dc.type | Dissertation |