# Optimizing Cervical Cancer Diagnosis with Feature Selection and Deep Learning
## 4 Results
### Feature Vector Reduction Results  
To improve the computational efficiency and performance of our classifiers, we implemented five techniques to decrease the dimensions of our feature vectors. For this purpose, we implemented a multifaceted approach, integrating statistical tests and algorithmic feature selection techniques to streamline our feature set. Specifically, the implementation of the Kruskal-Wallis test, Kolmogorov-Smirnov test, and Friedman test provided a robust statistical framework to identify features with significant differences. In addition, stepwise feature selection and significant feature selection methods were utilized to refine the feature vectors. The following description details the results of these reduction methods and highlights their impact on model performance.  
At first, features reflecting the real morphometric features of the cells were calculated. These features resembled the characteristics evaluated by a specialist when making a diagnosis, which led to the calculation of 27 morphometric features. Upon examining the characteristics of these features, it becomes evident that the importance of extracted features varies when classifying cervical cancer. In Table 2, we summarize the features that are the most important according to the test used for size reduction. The purpose of our experiments was to check whether the reduced feature vector provides information for accurate cancer classification in the automated diagnosis task. To accurately assess the classification performance of the reduced feature vectors, a reduction rate metric has been introduced. This metric indicates the extent of the reduction by representing the percentage of features omitted from the entire feature vector. The reduction rate is visualized in Figure 4.  
From the results presented in Table 2, it is evident that both stepwise feature selection and significant feature selection provide the most substantial reduction of the set, at 74.1% and 40.7%, respectively. A detailed examination of the classification results for the reduced feature set will be discussed in the next subsection.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Test** & \multicolumn{2}{c}{**Chosen Features**} & \multicolumn{2}{c}{**Reduction Rate**} \\ \hline [PERSON] & N, C, NCr, Np, Cp, NCp, NMinA, CMinA, NCMinAr, NMaxA, & & 29.6\% \\  & CMaxA, NCMax, NEXT, NSol, Col, NCSol, Neq, Ceq & & & \\ \hline [PERSON]–[PERSON] & N, C, NCr, Np, Cp, NCp, NMinA, CMinA, NCMinAr, NMaxA, CMax, & & 22.2\% \\  & NCMax, NEXT, CEXT, NSol, Col, NCSol, Neq, Ceq & & & \\ \hline [PERSON] & N, C, NCr, Np, Cp, NCp, NMnA, CMinA, NCMinAr, NMaxA, CMax, & & 25.9\% \\  & NCMax, NEXT, NCExt, NSol, Col, NCSol, Neq, Ceq, NCeq & & & \\ \hline Stepwise feature selection & N, C, NCMinAr, CMaxA, NEXT, Neq, Ceq & & & 74.1\% \\ \hline Significant feature selection & NCr, Cp, NCp, NMinA, CMinA, NCMinAr, NMaxA, CMax, & & & 40.7\% \\  & NCMax, CAr, NCExt, NSol, NCSol, Neq, Ceq, NCeq & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Reduction rates for feature selection methods.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **CNN** & **Baseline** & **KW** & **KS** & **Fr** & **SFS** & **SBS** \\ \hline NN & 92.9\% & 82.7\% & 87.7\% & 86.3\% & 88.1\% & 89.7\% & 88.1\% \\ SVM & 93.3\% & 90.1\% & 90.9\% & 91.8\% & 91.4\% & 90.1\% & 93.0\% \\ KNN1 & 87.1 \% & 80.7\% & 88.9\% & 88.5\% & 88.9\% & 86.8\% & 85.6\% \\ KNN10 & 84.4 \% & 84.0\% & 88.5\% & 87.7\% & 88.1\% & 86.8 \% & 86.8\% \\ Disc. An. & 90.9\% & 87.2\% & 86.4\% & 87.2\% & 87.7\% & 87.2\% & 87.7\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Classification results with respect to feature selection methods.