## 1 Introduction  
Infectious diseases are threats to global health: one is tuberculosis (TB), a serious contagious disease that can be easily transmitted. TB is spread through the air when a TB infected person coughs, sneezes or spits. It is caused by the _Mycobacterium tuberculosis_. The World Health Organization (WHO) estimated that around 11 million people were ill with TB in 2019 worldwide and 1.5 million died. The early and accurate detection of TB is essential to control the disease progression and to prevent forward transmissions. The WHO recommends that a chest x-ray is an essential tool to end TB. Chest x-rays are non-invasive, fast, affordable, highly sensitive and widely available in urban areas. Chest x-rays are used to screen or triage TB in the lungs and to follow up the treatment. TB mostly affects the lungs and when TB lesions appear in the lungs, the chest x-rays will display abnormal grey or white shadows [1; 2]. Conventional examination of chest x-rays needs a high degree of expertise, takes time and is prone to human errors. With the advent of digital imaging and advanced computer vision methods, computer-assisted diagnosis (CAD) of the diseases, computational techniques have been evaluated in several types of research. CAD systems aim to assist the medical experts in identifying the disease and to serve as a complement to human reading of medical images. With the support of CAD systems, we can detect TB accurately and rapidly, and possibly prevent further transmissions, if it is detected early. CAD systems have the potential to expedite mass screening programs in high TB prevalence areas, where a large number of x-rays need to be examined. They can also alleviate the shortage of qualified medical staff, especially in remote areas [3]. Previous studies of automatic TB prediction in chest x-rays may be categorized into two groups: (i) hand-crafted feature-based machine learning methods and (ii) deep convolutional neural network (CNN) based methods. Both categories work with or without lung segmentation. For the first category, [PERSON] et al. [4] used multi-scale feature banks as the inputs to a weighted nearest-neighbor classifier to identify the presence of TB and obtained area under curve (AUC) of 98.6% and 82% on two private datasets. An algorithm by [PERSON] et al. [5] analyzed texture abnormality at the pixel level and provided AUCs between 67% and 86%. [PERSON] et al. [6] used a user-guided snake algorithm for lung segmentation, and first-order statistical features as features to be classified by a decision tree classifier for TB prediction. On a small custom dataset, their accuracy was 94.9%. [PERSON] et al. [7] used optimized graph cut methods for lung segmentation and object detection inspired features and content retrieval ones as input to a support vector machine (SVM) classifier. They obtained AUCs of 86.9% for the Montgomery County (MC) dataset and 90% for Shenzen datasets. [PERSON] et al. [8] segmented the lung using an atlas-driven method and extracted multi-level features of shape, curvature and Hessian matrix eigenvalues. Following this, the wrapper type feature selection with a multi-layer perceptron was used to select the discriminant features and differentiate between normal and TB chest x-rays: they achieved AUCs of 87% for MC and 99% for Shenzen. [PERSON] et al. [9] segmented the lung region using an atlas-driven method, extracted a combined feature set of texture and shape, and applied SVM as a classifier and obtained AUC of 93.4% for the Shenzen dataset. [PERSON] [10] used another texture feature-based method, after segmenting the lung region by thresholding; he extracted the textural features, and then differentiated between positive and negative using SVM, achieving AUCs of 71% for MC and 91% for Shenzen. [PERSON] and [PERSON] [11] used multi-scale features of shape, edge, and texture, and a combination of a Bayes network, Multilayer Perceptron (MLP) and a random forest, yielding AUCs of 90% for MC and 96% for Shenzen dataset.