## 4 Experimental Results of the Proposed Systems
### Results of the Hybrid Systems Approach  
This section summarizes the results of hybrid systems consisting of two parts: ResNet-50 and GoogLeNet models for feature extraction. The second part is the SVM algorithm for classifying the features extracted from the first part. The hybrid systems are called ResNet-50 + SVM and GoogLeNet + SVM. What distinguishes this method is its speed in  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Phase** & \multicolumn{4}{c}{**Training Phase**} \\ \hline
**Datasets** & \multicolumn{2}{c}{**First Dataset**} & \multicolumn{2}{c}{**Second Dataset**} \\ \hline
**Classes** & **Normal** & **Tuberculosisis** & **Normal** & **Tuberculosisis** \\ \hline Before augmentation & 209 & 215 & 2240 & 448 \\ After augmentation & **2090** & **2150** & **2240** & **2240** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Data augmentation for X-ray tuberculosis images to balance the two datasets.  
diagnosing the dataset, its implementation on a medium-cost computer, and its high accuracy in diagnosis. The two hybrid systems were evaluated based on the two tuberculosis datasets.  
Table 3 summarizes the performance results of the hybrid approaches for diagnosing the two tuberculosis datasets. It is noted that GoogLeNet + SVM achieved better results than ResNet-50 + SVM for the first dataset, while ResNet-50 + SVM performed better than GoogLeNet + SVM for the second dataset. The systems performance results are as follows: first, for the first dataset, ResNet-50 + SVM reached an accuracy of 95.5%, a sensitivity of 96.14%, a precision of 95.72%, a specificity of 95.84%, and an AUC of 97.28%. In comparison, GoogLeNet + SVM achieved an accuracy of 97%, a sensitivity of 97.21%, a precision of 97.45%, a specificity of 97.38%, and AUC of 98.13%. Second, for the second dataset, ResNet-50 + SVM reached an accuracy of 98.1%, a sensitivity of 95.52%, a precision of 97.11%, a specificity of 95.61%, and an AUC of 98.43%. In comparison, GoogLeNet + SVM achieved an accuracy of 97.1%, a sensitivity of 95%, a precision of 94.5%, a specificity of 94.85%, and an AUC of 97.63%.  
Figure 6 displays the results of performing the hybrid approaches to classify the two X-ray datasets for the early detection of tuberculosis.  
Figure 7 presents the confusion matrix generated using the hybrid approaches (ResNet-50 + SVM and GoogLeNet + SVM) based on the first dataset for tuberculosis diagnosis. The ResNet-50 + SVM system reached an accuracy of 92.3% and 98.5% when classifying normal and tuberculosis classes, respectively. In contrast, GoogLeNet + SVM system achieved an accuracy of 96.9% and 97% when classifying normal and tuberculosis classes, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Datasets** & \multicolumn{2}{c}{**First Dataset**} & \multicolumn{2}{c}{**Second Dataset**} \\ \hline
**Measure** & **ResNet-50 + SVM** & **GoogLeNet + SVM** & **ResNet-50 + SVM** & **GoogLeNet + SVM** \\ \hline Accuracy \% & 95.5 & 97 & 98.1 & 97.1 \\ Sensitivity \% & 96.14 & 97.21 & 95.52 & 95 \\ Precision \% & 95.72 & 97.45 & 97.11 & 94.5 \\ Specificity \% & 95.84 & 97.38 & 95.61 & 94.85 \\ AUC \% & 97.28 & 98.13 & 98.43 & 97.63 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of the performance of hybrid systems on the two tuberculosis datasets.  
Figure 6: Results of the hybrid approaches to diagnosing tuberculosis based on the two datasets.  
Figure 8 presents the performance of the hybrid approaches (ResNet-50 + SVM and GoogLeNet + SVM) based on the second dataset for tuberculosis diagnosis. The ResNet-50 + SVM system reached an accuracy of 99.3% and 92.1% when classifying normal and tuberculosis classes. In contrast, GoogLeNet + SVM system reached an accuracy of 98.1% and 92.1% when classifying normal and tuberculosis classes.