## 5 Discussion and Comparison of the Implementation of the Proposed Techniques  
This study deals with modern and effective methodologies for classifying two X-ray datasets for the early detection of tuberculosis using two proposed approaches; each approach contains two different systems. Initially, due to the images containing noise and low contrast, all images of the two datasets were enhanced. Due to the scarcity of images in the two datasets, the data augmentation method was used for two purposes: to overcome the limitation of the lack of images, and to address the problem of unbalanced classes in the two datasets. The first proposed approach is a hybrid method consisting of deep learning models (ResNet-50 and GoogLeNet), which receive optimized images and extract deep features through convolutional and auxiliary layers and store them in feature vectors. The PCA method was used to reduce the high dimensionality of the deep features. The last layers of the ResNet-50 and GoogLeNet models were removed and replaced with an SVM method that receives low-dimensionality features and classifies them with high accuracy and efficiency. Thus, the first proposed approach consists of two hybrid systems, ResNet-50 + SVM and GoogLeNet + SVM. The second proposed approach is based on the hybrid features of several techniques and their classification by the ANN classifier. The second proposed approach consists of a single system, an ANN, which is fed with two different hybrid feature methods: first, a hybrid feature extracted by ResNet-50, GLCM, DWT and LBP; and second, the hybrid features extracted by GoogLeNet, GLCM, DWT and LBP. The first proposed approach reached an overall accuracy of 95.5% and 97% for the two hybrid systems, namely ResNet-50 + SVM and GoogLeNet + SVM, respectively, when classifying the first dataset. Additionally, an overall accuracy of 98.1% and 97.1% was obtained for the two-hybrid systems, namely ResNet-50 + SVM and GoogLeNet + SVM, respectively, for the classification of the second dataset. The performance of the the second proposed is approach as follows: first, for the first dataset, the ANN classifier achieved an overall accuracy of 99.2% when fed with hybrid features from ResNet-50, GLCM, DWT and LBP; in contrast, it achieved an overall accuracy of 98.5% when fed with hybrid features from GoogLeNet, GLCM, DWT and LBP. Second, for the second dataset, the ANN classifier achieved an overall accuracy of 99.8% when fed with hybrid features from ResNet-50, GLCM, DWT and LBP; in contrast, it achieved an overall accuracy of 99.3% when fed with hybrid features from GoogLeNet, GLCM, DWT and LBP. Table 5 describes the performance results of all of the proposed approaches for classifying the two X-ray datasets for the early detection of tuberculosis. First, for the first dataset, an ANN classifier with features of ResNet-50, GLCM, DWT and LBP achieved the best accuracy when classifying normal image classification of 98.5%, whereas ANN, based on the features of both methods, achieved the best accuracy for the classification of tuberculosis images of 100%. Second, for the second dataset, an ANN classifier with features from ResNet-50, GLCM, DWT and LBP achieved the best accuracy in normal image classification of 100% and the best accuracy in tuberculosis image classification of 98.6%. Figure 15 presents the performance of all of the proposed X-ray diagnostic systems for the two tuberculosis datasets. Table 6 and Figure 16 summarize a comparison between the performance of the proposed system in classifying the tuberculosis datasets and that of the systems in previous relevant studies. It is noted that the performance of our proposed system outperforms all previous systems. It is also noted that our systems were evaluated with many evaluation criteria, while the systems of previous studies were limited to some evaluation criteria. Previous systems achieved an accuracy of between 77% and 96.20%, while ours achieved an accuracy of 99.8%.