## 2 Related Work  
The diagnosis of tuberculosis is one of the hot topics in the literature. Researchers have proposed several approaches with the aim of developing systems with satisfactory diagnosis accuracy. However, researchers and other interested parties continue to strive to reach an appropriate accuracy that may enhance diagnosis decisions. As we mentioned before, what distinguishes our study from other is extracting features from deep learning models and combining them with features of texture, shape and geometry extracted by GLCM, DWT and LBP algorithms and classifying them using hybrid techniques for the accurate and effective diagnosis of tuberculosis. In this part, we will discuss several studies related to the diagnosis of tuberculosis using deep learning. For instance, [PERSON] et al. [9] presented a DCNN model for diagnosing chest X-rays and testing it on another dataset. They concluded that the deep learning models developed based on a training dataset do not have the same performance when applied to another dataset. The advantage of this technology is that it is developed, but the disadvantage is that it is appropriate for one dataset and it cannot be applied to another dataset. [PERSON] et al. [10] developed three machine learning algorithms to predict tuberculosis based on a dataset from several hospitals in Punjab, India. The decision tree achieved the best performance for diagnosing tuberculosis, with an accuracy of 95%. This approach's advantage is that it uses more than one algorithm for classification, but the disadvantage of all of the techniques is that they involve machine learning only. [PERSON] et al. [11] proposed a ResNet model for diagnosing tuberculosis based on chest X-ray images using the features extracted by the Haar and LBP methods. The dataset was augmented by a pipeline based on intuitive features. [PERSON] et al. [12] proposed an SVM algorithm to distinguish pulmonary tuberculosis from lung disease. The algorithm classifies images based on their discriminatory features. The algorithm's performance indicates that bronchiectasis is useful in discovering two important characteristics; the algorithm achieved an accuracy of 85% and a sensitivity of 88%. This technique's advantage is that it extracts representative features, but the disadvantage is that it does not achieve satisfactory accuracy. [PERSON] et al. [13] developed improved SVM algorithms to classify chest X-rays based on texture features extracted by the WT method. A genetic algorithm was applied to select representative features. The selected features were fed to the SVM algorithm for their classification. The advantage of this method is that it applies an algorithm to select the features, but its disadvantage is that the selected features are not tested on more than one classifier. [PERSON] et al. [14] developed an EfficientNets model to diagnose tuberculosis based on two detection datasets. Five types of EfficientNets were implemented; efficientNet-b4 achieved the best performance, with an accuracy of 92.33%. The advantage of this approach is the application of several EfficientNets to diagnose tuberculosis, but its disadvantage is that did not reach high accuracy. [PERSON] et al. [15] presented the deep convolutional network based on the generative adversarial network, which consists of three different structures to optimize, generate, segment, and classify chest X-ray images. Data augmentation and parameter tuning based on a genetic algorithm have been applied to improve diagnostic results; this method achieved an accuracy of 97.1%. [PERSON] et al. [16] proposed a hybrid algorithm for extracting features from X-ray images of tuberculosis patients. Then, this method segmented lung regions using DeepLabv3 and performed feature extraction using six handcrafted feature extraction methods and nine deep feature extraction models. Particle swarm optimization was applied to select the dominant features from each model.