An efficient deep neural network model for tuberculosis detection using chest X-ray images

Tuberculosis caused by the infection of Mycobacterium. It is the fifth major source of death and one of the greatest threats to humans in the modern world. Thus, it needs to be detected at an earlier stage using the chest X-rays (CXR) image for precise identification and treatment. The suggested scheme's main goal is to identify this deadly disease using CXR with improved classification accuracy. This detection process comprises pre-processing, noise removal, balancing of image level, application of the Double Attention Res-U-Net-based Deep Neural Network (DARUNDNN) model, and optimization of deep learning features using the Dingo Optimization Algorithm for achieving better accuracy. The experimental validation of the proposed DARUNDNN model is conducted using benchmark datasets, namely Montgomery, Shenzhen, and National Institutes of Health CXR images. The results obtained using the Shenzhen dataset confirm that the proposed DARUNDNN model is efficient in achieving better accuracy of 98.92%, specificity of 97.24%, and sensitivity of 98.86% with a least error of 1.6 compared to the benchmarked models used for investigation. Moreover, the experimental validation conducted using the Montgomery County dataset also confirmed an excellent accuracy of 98.982%, a specificity of 97.56%, and a sensitivity of 98.52%, with a least error of 1.32 compared to the baseline approaches used for investigation.