Enhancing Diagnostic Accuracy of Drug-Resistant Tuberculosis on Chest X-rays Using Data-Efficient Image Transformers

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Abstract

Tuberculosis is an infectious disease with increasing fatalities around the world. The diagnosis of the disease
is a major challenge to its control and management due to the lack of adequate diagnostic tools, contributing
significantly to the prevalence of drug-resistant tuberculosis. Convolutional Neural Network (CNN) models
have recently been developed to detect drug-resistant tuberculosis by analyzing chest radiograph images from
the TB portal, but the classification results are low. This is because CNNs struggle to capture complex global
and overlapping features in medical imaging, such as chest radiographs of drug-resistant tuberculosis. In
contrast, transformers excel in these areas by utilizing self-attention mechanisms that detect inherent subtle and
long-range dependencies across images. In this study, we used a pretrained data-efficient image transformer
(DEiT) model to enhance the diagnosis of drug-resistant tuberculosis and differentiate it from drug-sensitive
tuberculosis. The new model achieved an AUC of 80% in the detection of drug-resistant tuberculosis, an
improvement of 13% in the AUC compared to current CNN models using data from the same source. The
bootstrap significance test shows that the difference in AUCs is statistically significant. The results of the
study can help healthcare providers improve drug-resistant tuberculosis diagnostic accuracy and treatment
outcomes.
Original languageEnglish
Number of pages11
Publication statusAccepted/In press - 4 Dec 2024
Event18th International Conference on Health Informatics - Porto, Portugal
Duration: 20 Feb 202522 Feb 2025

Conference

Conference18th International Conference on Health Informatics
Country/TerritoryPortugal
CityPorto
Period20/02/2522/02/25

Keywords

  • Tuberculosis, Drug Resistance, Deep Learning, Vision Transformer, Data-Efficient Image Transformer, Transfer Learning, Chest X-rays

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