## 4 Results of System Performance
### Results of CNN Models with SVM  
The performance hybrid system VGG-16 + SVM and GoogLeNet + SVM is for diagnosing WSIs of LBC slice samples of abnormal cells of cervical cancer. The system consists of VGG-16 and GoogLeNet models, which are tasked with extracting features, and SVM, which is tasked with classifying features. It is worth noting that the SVM classifier has been replaced by the last classification layer in the VGG-16 and GoogLeNet models. This approach was used because of its high performance in obtaining satisfactory results and speed in training the data set and implementing it on a medium-cost computer.  
Table 3 shows the results of the two-hybrid networks for the classification of the cervical cancer data set. The VGG-16 + SVM system is slightly better than the GoogLeNet + SVM system. The VGG-16 + SVM system yielded an accuracy of 97.5%, specificity of 99.4%, sensitivity of 97.6%, an AUC of 98.43% and precision of 97.72%. In contrast, the GoogLeNet + SVM system yielded an accuracy of 96.8%, specificity of 99.2%, sensitivity of 96.8%, an AUC of 98.21% and precision of 96.9%.  
Figure 7 presents the diagnostic performance of the VGG-16 + SVM and GoogLeNet + SVM networks for classifying abnormal cells of cervical cancer.  
Figure 8 shows the result of the hybrid method through the confusion matrix for diagnosing five types of abnormal cells of the cervix. The VGG-16 + SVM reached a  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Measure** & **VGG-16** & **GoogLeNet** \\ \hline Accuracy \% & 91.22 & 95.96 \\ Specificity \% & 97.6 & 98.64 \\ Sensitivity \% & 91.41 & 95.45 \\ Precision \% & 91.68 & 95.81 \\ AUC \% & 93.45 & 96.32 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of the CNN models for cervical cancer data set diagnosis.  
Figure 7: Display of the performance of CNN with SVM for classifying abnormal cervical cells.  
diagnostic accuracy for each class by 97.3%, 98%, 97.1%, 97.1% and 98.2% for classifying cervix_dyk, cervix_koc, cervix_mep, cervix_pab and cervix_sfi, respectively. In contrast, GoogLeNet + SVM achieved a diagnostic accuracy for each class by 97.2%, 98.2%, 96%, 96% and 96.6% for classifying cervix_dyk, cervix_koc, cervix_mep, cervix_pab and cervix_sfi, respectively.  
Regarding the samples that the VGG-16 + SVM system failed to diagnose correctly: For the cervix_dyk class, the system failed to diagnose 27 images correctly. For class cervix_koc, the system failed to diagnose 20 images correctly. For class cervix_mep, the system failed to diagnose 29 images correctly. For the class cervix_pab, the system failed to diagnose 29 images correctly. For the cervix_gfi class, the system failed to diagnose 18 images correctly.  
Regarding the samples that the VGG-16 + SVM system failed to diagnose correctly: For the cervix_dyk class, the system failed to diagnose 28 images correctly. For class cervix_koc, the system failed to diagnose 18 images correctly. For class cervix_mep, the system failed to diagnose 40 images correctly. For the class cervix_pab, the system failed to diagnose 40 images correctly. For the cervix_sfi class, the system failed to diagnose 34 images correctly.