A Novel Approach for the Detection of Tuberculosis and Pneumonia Using Chest X-Ray Images for Smart Healthcare Applications

The early discrimination of pneumonia and tuber-culosis is difficult for radiologists due to similar pathological symptoms in the chest X-ray images. Therefore, there is a requirement for automated methods to detect and classify these two diseases accurately. This letter proposes the multiscale eigendomain gradient boosting (MEGB)-based approach to detect pneumonia and tuberculosis from chest X-ray images. The discrete wavelet transform is employed to evaluate subbands of chest X-ray images at different decomposition levels. The singular value decomposition (SVD) is utilized in each subband to evaluate singular values, left eigenmatrix, and right eigenmatrix, respectively. The maximum value of each column of both left and right eigenmatrices and singular values for each subband of the X-ray image are used as features. All subband eigendomain feature vectors are concatenated and given to the light gradient boosting model to detect pneumonia and tuberculosis diseases. The performance of the proposed MEGB approach-based detection is evaluated using chest X-ray images from a publicly available database. The suggested MEGB approach has achieved an accuracy value of 96.42%. The suggested approach performs better than the transfer learning and other reported methods to detect pneumonia and tuberculosis using chest X-ray images.

I. Introduction

The Internet of Things (IoT) is vital in developing intelligent healthcare systems for patient monitoring and diagnosing various diseases using physiological signals and medical images [1]. Tuberculosis is a bacterial infection disorder that affects the person's lungs [2]. Similarly, pneumonia disease is a type of respiratory infection that occurs due to viruses or bacteria [3]. Chest X-ray imaging is widely used in the clinical standard to screen pneumonia, tuberculosis, and other thoracic diseases [3], [4]. Portable radiography imaging has been utilized to diagnose COVID and other diseases [5]. The pathological changes, such as the enhanced bronchovascular markings, consolidation in lungs, and alveolar infiltrates (affected areas of the lung look cloudy) in the chest X-ray images are used to detect pneumonia. Similarly, for tuberculosis, cavitary lesions (dark and fluid-filled regions within lung opacities), pleural effusion, and right-sided infiltration are observed in the chest X-ray images. Chest X-ray images are frequently generated in hospitals with large numbers of patients. It is a time-consuming task for radiologists to manually investigate the chest X-ray of each subject to diagnose tuberculosis and other thoracic diseases [4]. Therefore, automated approaches based on artificial intelligence (AI) algorithms are used to assist radiologists in predicting the type of disease from the chest X-ray images [4]. Developing novel AI-based methods for the automated detection of different thoracic diseases from chest X-ray images is important for IoT-based smart healthcare applications.