# A Hierarchical Feature-Based Methodology to Perform Cervical Cancer Classification
## 4 Conclusions  
This work proposes a hierarchical classification methodology to classify nuclei of Pap smear images using handcrafted features. As mentioned before, in the cytopathologist's routine, image analysis is entirely manual and subjective, a tiring and monotonous task. The proposal performs a computational screening procedure capable of excluding irrelevant nuclei images to identify possible lesions and reduce the number of images analyzed visually by the cytopathologist. The reduction of the professional workload helps to focus attention on the analysis of relevant images, decreasing false-negative rates. The experiment indicates that hierarchical classification improves the results when compared with those without hierarchy. Considering the Herlev database, the results outperform the literature methods for 5-class and 7-class classification concerning the precision, recall, F1, accuracy, and specificity metrics. For the 2-class classification, our RF hierarchical method achieves the best results for two of the five metrics and had competitive results in the other metrics. Analyzing the metrics in which our method does not present the best result, we realize that the best result comes from a different method from the literature. However, even the best result for the specific metric performs poorly in all other metrics when compared to our method. Additionally, this work introduces the CRIC segmentation cervix collection and presents 2-class, 3-class, and 6-class classification results considering precision, recall, F1, accuracy, and specificity metrics. The present findings of cell nuclei classification suggest enhancing our understanding of the handcrafted features used in the machine learning algorithm. The hypothesis that features should be inspired in the biological criteria for differentiating abnormal cells from normal ones proved to be a feasible solution. The feature vector included a combination of nuclear contour shape morphologies with chromatin distribution (texture), and all attributes were used in the classification task. We tested eight machine learning traditional classifier methods to perform the nuclei classification and chose the four best ones (Decision Tree, k-NN, Random Forest, and Ridge) to report their results in this work concerning the hierarchical classification proposed. A statistical analysis shows that the Random Forest is the best one to classify nuclei images of the Herlev and CRIC databases regardless of the number of classes. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Classes** & **Method** & **Prec.** & **Rec.** & **F1** & **Acc.** & **Spec.** \\ \hline \multirow{8}{*}{2} & RF hierarchical & **0.9843** & 0.9841 & **0.9842** & 0.9842 & 0.9842 \\ \cline{2-7}  & CNN-ELM [17] & & & & **0.9950** & \\ \cline{2-7}  & Ensemble [21] & & 0.9787 & & 0.9783 & **0.9935** \\ \cline{2-7}  & GoogLeNet [18] & & & & 0.9450 & \\ \cline{2-7}  & GoogLeNet [22] & & & & 0.9617 & \\ \cline{2-7}  & Ensemble [24] & 0.9688 & **0.9896** & 0.9313 & 0.9651 & 0.8967 \\ \cline{2-7}  & Bayesian [24] & 0.9063 & 0.9778 & 0.7250 & 0.8798 & 0.6042 \\ \cline{2-7}  & SVM [24] & 0.9375 & 0.9793 & 0.9052 & 0.9520 & 0.8750 \\ \cline{2-7}  & k-NN [24] & 0.8136 & 0.8049 & 0.9000 & 0.8939 & 0.8182 \\ \hline \multirow{2}{*}{5} & RF hierarchical & **0.8519** & **0.8494** & **0.8506** & **0.9567** & **0.9747** \\ \cline{2-7}  & Resnet-101 [23] & & & & 0.7450 & \\ \hline \multirow{2}{*}{7} & RF hierarchical & **0.8431** & **0.8400** & **0.8400** & **0.9543** & **0.9733** \\ \cline{2-7}  & CNN-ELM [17] & & & & 0.9120 & \\ \cline{2-7}  & Ensemble [21] & & 0.7743 & & 0.8154 & 0.9057 \\ \cline{2-7}  & GoogLeNet [18] & & & & 0.6450 & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of the Herlev results with literature methods. **Author Contributions:** Conceptualization, D.N.D., M.T.R., A.G.C.B., C.M.C., D.M.U., F.N.S.d.M. and M.J.F.S. ; Data curation, D.N.D., M.T.R., A.G.C.B., C.M.C. and M.J.F.S. ; Formal analysis, D.N.D., M.T.R., A.G.C.B., C.M.C. and M.J.F.S.