## 1 Introduction
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. Besides, the prediction scores were used to indicate the diseased regions using heat maps. [PERSON] et al. used the combined feature set of local/global features from chest x-rays and clinical information and selected the optimal feature subset using the minimum redundancy maximum relevant (mRMR) method [13]. Using the selected features, an ensemble classifier of random forest and extremely randomized trees was used to predict TB: they obtained AUCs of 99.6% for MC and 97.7 for Shenzen datasets. [PERSON] et al. [14] used GIST and pyramid histogram of oriented gradients (PHOG), as the feature extractors, and SVM, as the classifier, and tested two custom datasets: Dataset A and DataSet B. They achieved promising results using PHOG features with an accuracy of 92.3% for Dataset A and 92.0% for Dataset B. These studies and their results show that the handcrafted features are suitable for classifying normal and TB containing x-rays. In recent years, deep CNNs have achieved human experts-level performance on automated detection of diseases on a diversity of medical images. For example, [PERSON] et al. [15] achieved dermatologist-level performance on skin cancer detection and [PERSON] et al. [16] outperformed the conventional methods and achieved the radiologist-level pneumonia detection. We found four different strategies using CNNs to detect diseases in medical images: training the new CNN architecture from scratch, fine-tuning of pre-trained CNNs, use of pre-trained CNNs as the feature descriptors and the integration of deep features from pre-trained CNNs with shallow hand-crafted features. [PERSON] et al. described a modified CNN model, a variant of AlexNet, with a depth of five convolutional blocks and additional skip connections [17]; they achieved AUCs of 81.1% for MC and 90% for Shenzen datasets and used their model to generate the saliency maps and gradient-weighted class activation mapping (Grad-CAM) to localize the disease regions. Despite the promising results, training the model from scratch presents significant challenges, because it demands the high computational resources and a large volume of data. Training the model from scratch using a small dataset may over-fit. When there is only a small dataset available, fine-tuning of the pre-trained CNNs on certain images is an alternative to training the network from scratch, while achieving similar results. For example, [PERSON] et al. [18] adopted the transfer learning using the pre-trained AlexNet and achieved AUCs of 88.4% for MC and 92.6% for Shenzen datasets. [PERSON] et al. [19] used Alexnet, VGG16, VGG19, ResNet-18, ResNet-50, and ResNet-152 CNN models separately and combined their results to make a final prediction decision, using the Shenzen dataset, they found an AUC of 94%. Similarly, [PERSON] and [PERSON] [20] used an ensemble learner, combining the output of AlexNet and GoogLeNet, and evaluated it on four different datasets with 1007 images, they achieved AUC of 99%. Even fine-tuning the pre-trained models, it took time to train and parameters to tune. Therefore, [PERSON] et al. [21] and [PERSON] et al. [22] showed how to use pre-trained CNNs as the feature extractors, this technique is called deep-activated features or deep features. [PERSON] et al. [21] used the pre-trained CNNs as the features descriptors. Deep activated features from GoogleNet, ResNet, and VGGNet were extracted in three different approaches and fed separately as input to the SVM classifier. They obtained the highest AUC of 92.6% for MC and Shenzen datasets. [PERSON] et al. [22] first segmented the lung region, using an atlas-based method, and built the SVM classifiers using hand-crafted features, and deep-activated features from pre-trained CNN models, separately. Finally, they built an ensemble-stacked model of different based learners via majority voting.