An Improved Densenet Deep Neural Network Model for Tuberculosis Detection Using Chest X-Ray Images

Abstract

Tuberculosis (TB) is a highly contagious and life-threatening infectious disease that affects millions of people worldwide. Early diagnosis of TB is essential for prompt treatment and control of the spread of the disease. In this paper, a new deep learning model called CBAMWDnet is proposed for the detection of TB in chest X-ray (CXR) images. The model is based on the Convolutional Block Attention Module (CBAM) and the Wide Dense Net (WDnet) architecture, which has been designed to effectively capture spatial and contextual information in the images. The performance of the proposed model is evaluated based on a large dataset of chest X-ray images and it is compared to several state-of-the-art models. The results show that the proposed model outperforms the other models in terms of accuracy (98.80%), sensitivity (94.28%), precision (98.50%), specificity (95.7%) and F1 score (96.35%). Additionally, our model demonstrates excellent generalization ability, with consistent performance on different datasets. In conclusion, the proposed CBAMWDnet model is a promising tool for the early diagnosis of TB, with superior performance compared to other state-of-the-art models, as evidenced by the evaluation metrics of accuracy, sensitivity, and specificity.

Keywords

• Chest X-ray
• convolutional neural network
• deep learning
• disease diagnosis
• tuberculosis