## 5 Results
### Configuration III SVM Classifiers Performance  
The performance of the SVMs trained with the fusion of the three DL feature sets with the combined handcrafted features using PCA is shown in this section. An ablation study was made to show the number of PC versus the diagnostic accuracy and is displayed in Table 3. As illustrated in Table 3, for the linear SVM the highest accuracy of 99.7% was attained with 20 PCs, whereas for quadratic SVM, the peak accuracy of 100% was achieved with 35 PCs. On the other hand, for the cubic SVM, the maximum accuracy of 99.9% was accomplished by 40 PCs. In the case of gaussian SVM, 99.3% peak accuracy was reached with 10 PCs. These accuracies were greater than those obtained in the previous scenarios, except for the gaussian SVM. The studies [45; 68] showed that combining multiple CNN features with multi-domain handcrafted features is capable of enhancing the diagnostic performance, therefore the results demonstrated in Table 3 were better than those obtained in Tables 1 and 2, which were based on either using handcrafted features only, or merging deep features attained from a single CNN with handcrafted features. This is because using hand-crafted features may only prevent the classification model from mining the interrelatedness of cervical cancer features. Furthermore, the current CADs are mostly relying on single models working in the time/spatial domain, while hybrid approaches operating in multiple domains can attain better performance [45]. On the other hand, despite the fact that DL models produce promising results, depending on deep features alone has limitations. This is because the obtained deep features lose physical significance and arbitrary previous understanding. The CNN structure has many parameters and is heavily reliant on massive data and manual labels, which adds extra to the challenges in medical applications [45]. \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **Linear** & **Quadratic** & **Cubic** & **Gaussian** \\ \hline ResNet-18 & 98.4 & 98.5 & 98.5 & 98.4 \\ \hline ResNet-18 + Combined & 98.5 & 99.1 & 99 & 98.6 \\ Handcrafted & 97.6 & 97 & 96.9 & 97.7 \\ \hline DarkNet-19 + Combined & 99.2 & 99.6 & 99.6 & 97.9 \\ Handcrafted & 97.2 & 96.8 & 96.8 & 97.3 \\ \hline MobileNet + Combined & 99.1 & 99.2 & 99.2 & 98.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The diagnostic accuracy (%) of the SVM classifiers trained with independent DL Feature sets and concatenated with the combined handcrafted features set from multiple domains. \begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c} \hline \hline  & & & & & & & & & & & & & & & \\ \hline Classifiers & 5 & 10 & 15 & 20 & 25 & 30 & 35 & 40 & 45 & 50 & 55 & 60 & 65 & 70 \\ \hline Linear & 71.8 & 99.4 & 99.6 & 99.7 & 99.5 & 99.6 & 99.7 & 99.7 & 99.6 & 99.6 & 99.6 & 99.6 & 99.6 \\ \hline Quadratic & 81.4 & 99.3 & 99.6 & 99.6 & 99.3 & 99.5 & 100 & 100 & 99.8 & 99.7 & 99.7 & 99.8 & 99.8 & 99.8 \\ \hline Cubic & 79.7 & 99.4 & 99.7 & 99.5 & 99.2 & 99.4 & 99.8 & 99.9 & 99.6 & 99.6 & 99.7 & 99.7 & 99.7 \\ \hline Gaussian & 81.1 & 99.3 & 99 & 99.2 & 98.6 & 98.5 & 98.4 & 98.4 & 98.6 & 98.4 & 98.6 & 98.6 & 98.5 & 95.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The confusion matrices for the linear, quadratic, cubic, and gaussian SVMs trained with the fused DL features and the combined handcrafted features via PCA. The confusion matrices for the linear, quadratic, cubic, and gaussian SVMs trained with the fused DL features and the combined handcrafted features via PCA are shown in Figure 3. The receiving operating characteristics (ROC) curve and the area under ROC (AUC) for the quadratic SVM classifier trained with the 35 PCs are shown in Figure 4. Figure 4 shows that the AUC is equal to 1. More evaluation indices were used to access the performance of the presented CAD and are revealed in Table 4. Such indices involve sensitivity, specificity, accuracy, F1-score, precision, and MCC. Table 4 demonstrates that all of these metrics were equal to 1 for the quadratic SVM.