ORCID

Abstract

Tuberculosis is an infectious disease with increasing fatalities around the world. The diagnosis of the diseaseis a major challenge to its control and management due to the lack of adequate diagnostic tools, contributingsignificantly to the prevalence of drug-resistant tuberculosis. Convolutional Neural Network (CNN) modelshave recently been developed to detect drug-resistant tuberculosis by analyzing chest radiograph images fromthe TB portal, but the classification results are low. This is because CNNs struggle to capture complex globaland overlapping features in medical imaging, such as chest radiographs of drug-resistant tuberculosis. Incontrast, transformers excel in these areas by utilizing self-attention mechanisms that detect inherent subtle andlong-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-sensitivetuberculosis. The new model achieved an AUC of 80% in the detection of drug-resistant tuberculosis, animprovement of 13% in the AUC compared to current CNN models using data from the same source. Thebootstrap significance test shows that the difference in AUCs is statistically significant. The results of thestudy can help healthcare providers improve drug-resistant tuberculosis diagnostic accuracy and treatmentoutcomes.

Publication Date

2024-12-04

Keywords

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

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