## 1 Introduction
Reported methods for automatic interpretation of TB range from conventional machine learning methods [4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14] to deep learning methods [15; 16; 17; 18; 19; 20]. Intuitively, interpretation pipelines have two steps: segmentation of the lung region and prediction of TB manifestations. Lung regions were mostly segmented by a lung atlas-driven method, and then the TB was predicted based on hand-crafted features, followed by a supervised classifier or deep learning method, or a hybrid of both. Typical hand-crafted features were shapes and textural features. [PERSON] et al. [4] used multi-scale feature banks for feature extraction, followed by a weighted nearest-neighbor classifier; they achieved an area under the receiver operating characteristic curve (AUC) of 98.6% and 82% on two private datasets. [PERSON] et al. [5] proposed an algorithm to detect the textural abnormality at the pixel level for TB diagnosis. The algorithm provided AUCs between 67% and 86%. [PERSON] et al. [6] segmented lung regions using a user-guided snake algorithm, used first-order statistical features, and a decision tree classifier to classify between normal and X-rays showing TB. On a small custom dataset, their highest accuracy was 94.9%. Unfortunately, those studies were hampered by a lack of publicly accessible datasets for comparison. In 2014, [PERSON] et al. made two datasets publicly available--SZ with 662 images and MC with 138 images [21]. These datasets led to an increased focus on automated TB detection in X-ray images. [PERSON] et al. first segmented the lung region, using optimized graph cut methods, then extracted two sets of different features: object detection-inspired features and content retrieval ones. Finally, those extracted features were fed to the support vector machine (SVM) classifier to classify either negative or positive TB and obtained AUCs of 86.9% for MC and 90% for SZ datasets [7]. [PERSON] et al. [8] sequentially combined the atlas-driven lung segmentation, the extraction of the multi-level features of shape, curvature and eigenvalues of the Hessian matrix, the wrapper feature selection, and a multi-layer perceptron (MLP) network and obtained promising results with AUCs of 87% for MC and 99% for SZ. A combination of shape and texture features was studied by [PERSON] et al. [9], who used an atlas-based method for lung segmentation and SVM as a classifier. The authors obtained an AUC of 93.4% for the SZ dataset. [PERSON] [10] used another texture feature-based method; after segmenting the lung region by thresholding, he extracted the textural features and, finally, differentiated between positive and negative using SVM and achieved AUCs of 71% for MC and 91% for SZ. [PERSON] and [PERSON] [11] used multi-scale features of shape, edge, and texture, and a combination of a Bayes network, MLP, and random forest to discriminate between negative and positive, yielding AUCs of 90% for MC and 96% for SZ. The multiple instance learning methods [12] used moments of pixel intensities as features and SVM as a classifier and led to AUCs between 86% and 91% for three private datasets. In addition, the prediction scores were used to indicate the diseased regions using heat maps. Recently, DCNNs were used for TB detection. They were based on the networks trained on ImageNet. [PERSON] et al. [13] first used the pre-trained AlexNet for the detection of TB and achieved AUCs of 88.4% for MC and 92.6% for SZ datasets. A variant of AlexNet architecture, with a depth of five convolutional blocks and additional skip connections, was presented by [PERSON] et al. [14] to obtain AUCs of 81.1% (MC) and 90% (SZ). They also generated the saliency maps and Grad-CAM to visualize the disease regions. [PERSON] et al. [15] analyzed three ways to use the pre-trained networks as the feature extractor. Features from GoogleNet, ResNet, and VGGNet were analyzed along with an SVM classifier--the highest AUC was 92.6% for both MC and SZ datasets.