## 5 Discussion of the Execution of the Systems  
This study discussed the development of several hybrid systems in novelty and efficiency for the diagnosis of WSIs of LBC glass slide samples for five types of abnormal cells for the cervical cancer data set. Data set images were improved, artifacts removed, and abnormal cells edge contrast was increased. This study was divided into three approaches, each with two systems, with different techniques and methodologies. A first approach was a hybrid approach consisting of two parts: the first part for deep feature extraction using the GoogLeNet and VGG-16. It is worth noting that the deep features extracted by GoogLeNet and VGG-16 are high-dimensional. Therefore, the PCA was used after the GoogLeNet and VGG-16 in all the approaches in the study to reduce the features. The second part was to classify low-dimensional deep features by SVM classifier. These hybrid techniques, GoogLeNet + SVM and VGG-16 + SVM, yielded an overall accuracy of 97.5% and 96.8%, respectively, for diagnosing the cervical cancer data set. The second approach cervical cancer image diagnosis used ANN according to features extracted using the VGG-16 and combined with features of the GoogLeNet model. The ANN classifier according to the feature combination of the VGG-16 and GoogLeNet models achieved accuracy of 98.5%. The third approach was the diagnosis of cervical cancer images using ANN according to the fused features of the VGG-16 model and its fusion with the hand-crafted features, in addition to the fused features of the GoogLeNet model and its fusion with the hand-crafted features. The ANN classifier, when fed with mixed features of VGG-16 with hand-crafted features achieved an accuracy of 99.4%, while when fed with the mixed features of GoogLeNet with hand-crafted features obtained an accuracy of 99.3%. It is noted that the diagnostic results improved when integrating the deep features of the two models, VGG-16 and GoogLeNet instead of using the features of each model alone. We also noticed that the diagnostic results improved when combining the deep features with hand-crafted features. Thus, the best performance of the classifier occurred when the deep learning features were fused with the features of the FCH, GLCM and LBP methods. Table 6 shows results of performing all the approaches in this study for diagnosing abnormal cells of the cervical data set. The table notes the overall diagnostic accuracy and  
Figure 14: Presentation of the executive of the ANN with fusion features for diagnosing abnormal cervical cells. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Fusion Features** & **VGG-16, FCH, GLCM and LBP** & **GoogLeNet, FCH, GLCM and LBP** \\ \hline Accuracy \% & 99.4 & 99.3 \\ Specificity \% & 100 & 100 \\ Sensitivity \% & 99.35 & 99.12 \\ Precision \% & 99.42 & 99.23 \\ AUC \% & 99.89 & 99.56 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results for the ANN with fusion of CNN with hand-crafted features. accuracy for each type of abnormal cells of the cervix. The best overall accuracy achieved by the ANN with hybrid features of VGG-16, FCH, GLCM and LBP was 99.4%. As for the accuracy for each class, the cervix_dyk and cervix_koc classes were diagnosed with an accuracy of 99.4% and 99.5%, respectively, by an ANN according to the hybrid features of GoogLeNet and hand-crafted. In contrast, the best accuracy for cervix_mep, cervix_pab and cervix_sfi classes was 99.5%, 99.6% and 99.4%, respectively, by ANN according to the hybrid features of VGG-16 and hand-crafted. Table 7 summarizes the results of the proposed methods and compares them with the results of previous methods of diagnosing WSIs for early detection of cervical cancer. It is noted that the performance of the proposed method is better than the previous methods. The previous methods obtained an accuracy between 83.1% and 98.27%; in contrast, the proposed method reached an accuracy of 99.4%. The previous methods achieved a specificity between 95.78% and 96%; in contrast, the proposed method reached a specificity of 99.4%.