## 1 Introduction
Using four datasets, they reported AUCs of 98.6% for MC, 99.4% for Shenzen, plus two additional datasets that they built--Kenya 82.9% and India 99.5%. The fourth strategy of using pre-trained CNNs, which is the focus of our work, was a combination of deep-activated features, extracted from pre-trained CNNs, with hand-crafted features, to design a more efficient and accurate classifier. Extracting multiple deep features from pre-trained CNNs and integrating them with shallow handcrafted features is a promising way to further enhance the performance, compared to using an individual feature set. Most of the hand-crafted and deep-activated features in this study have been used in TB detection. However, except the studies in [PERSON] et al. [8] and [PERSON] et al. [13], all extracted features were used as input to the classification, so the feature set might contain noise and irrelevant features. To address this lack, we used a particle swarm optimization (PSO) algorithm to select the discriminant features before classification. The hand-crafted features and deep-activated features from few pre-trained CNN models were studied by [PERSON] et al. [22]: they trained several classifiers using the hand-crafted and deep-activated features separately, and the output of multiple classifiers were fused for the a final decision. As an alternative, we exploited the combination of hand-crafted and deep-activated features to select a best hybrid feature set, from a diverse set of features, and input the hybrid feature set to a single SVM classifier. We ran several experiments using hand-crafted features from local and global feature descriptors, and deep activated features, from pre-trained CNNs, as input separately or in composite manner for a PSO feature selection algorithm and SVM classifier. These experiments showed that using a hybrid feature set, that contained the selected hand-crafted and deep-activated features performed better than an individual feature set. The four main contributions of this study are:  
* First, instead of using all extracted features as input, we selected the important features prior to classification. This is the first attempt to use the PSO feature selection algorithm for automated TB detection. By selecting the important features prior to classification, we reduced the noisy and irrelevant features, reduced processing times, and enhanced the prediction performance. Second, we optimized an SVM classifier using a Bayesian algorithm. * Third, we compared the classifications from hand-crafted and deep-activated features using the optimized SVM classifier. * Fourth, we combined the selected hand-crafted and deep-activated features to generalize the feature set in extensive experiments. To our knowledge, this is the first approach to predict TB using a hybrid feature set which contained a combination of selected handcrafted and deep-activated features. By using the hybrid feature set, we enhanced the prediction performance compared to individual methods and state-of-the-art. This paper has four sections. Section 2 presents the datasets. Section 3 describes the steps in our method: lung segmentation, feature extraction, feature selection and classification. Section 4 describes and discusses our experimental results. Section 5 concludes and suggests future work.