Tuberculosis classification on chest x-ray images using DenseNet-169 and convolutional block attention module

Abstract

Tuberculosis remains a major global health challenge, and the manual interpretation of chest X-rays is often limited by the subjectivity and shortage of radiology experts. While deep learning approaches like DenseNet have shown promise in medical imaging, the integration of attention mechanisms such as the Convolutional Block Attention Module (CBAM) for tuberculosis detection has been less explored. This study aimed to develop a Convolutional Neural Network (CNN) model utilizing DenseNet-169 combined with CBAM to accurately classify chest X-ray images into normal and tuberculosis classes. A dataset of 7,000 chest X-ray images was preprocessed and partitioned into training, validation, and testing sets. DenseNet-169 served as the backbone architecture, while CBAM was applied to emphasize crucial spatial and channel features. Evaluated across standard metrics, the proposed model achieved an accuracy of 99.43%, a precision of 99.72%, a recall of 99.14%, and an F1-score of 99.43%, successfully outperforming the baseline DenseNet-169 model without CBAM. Ultimately, the integration of CBAM with DenseNet-169 demonstrates remarkable potential in improving tuberculosis detection, confirming that attention mechanisms can substantially enhance deep learning performance in medical imaging.