# A Hierarchical Feature-Based Methodology to Perform Cervical Cancer Classification
## 3 Experiments and Results
### Oversampling Results  
Table 4 shows the performance results obtained using the balanced techniques and without any oversampling applied to the CRIC data. The 6-class classification is the most challenging; therefore, it was used in this experiment, aiming to provide greater differentiation in the results. As can been seen in Table 4, any oversampling technique improves the classification, but the best technique observed was the Borderline-SMOTE. For this reason, the Borderline-SMOTE was used as the oversampling technique in the experiments carried out. We only used this oversampling technique in the training data. The test set remained unchanged, guaranteeing the classification results' credibility. The balancing techniques applied to the Herlev database performed similarly to the CRIC database, so these results were not presented. Before balancing the data from the Herlev database with the Borderline-SMOTE technique, there were in the training base: 67 superficial squamous epithelial data; 63 intermediate squamous epithelial data; 83 epithelial columnar data; 164 mild squamous non-keratinizing dysplasia data; 132 moderate squamous non-keratinizing dysplasia data; 178 severe squamous non-keratinizing dysplasia data; and 135 squamous cell carcinoma in situ intermediate data. The largest class size (severe squamous non-keratinizing dysplasia with 178 data) was used as a reference size for balancing the data. Thus, after balancing, each of the seven classes comprised 178 data. These results were used in the 2-class balancing to allow all classes to be represented within the groups. In the 2-class balancing, we group the four abnormal classes, resulting in 712 (=4 \(\times\) 178) abnormal cells, with data equally distributed among the four classes. The group of normal classes resulted in 534 \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Technique** & **Prec.** & **Rec.** & **F1** & **Acc.** & **Spec.** \\ \hline Without oversampling & 0.58 & 0.53 & 0.53 & 0.90 & 0.94 \\ SMOTE & 0.79 & 0.80 & 0.79 & 0.93 & **0.96** \\
**Borderline-SMOTE** & **0.82** & **0.82** & **0.82** & **0.94** & **0.96** \\ SVM SMOTE & **0.82** & 0.80 & 0.81 & 0.93 & **0.96** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results for CRIC 6-class classification with/without oversampling. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Metric** & **Equation** & **Goal** \\ \hline Precision (Prec.) & \(\frac{TP}{TP+FP}\) & Indicate, among the positive ratings, the amount that is correct. \\ \hline Recall (Rec.) & \(\frac{TP}{TP+TN}\) & Indicates the correct detection of abnormal nuclei. \\ \hline F1-score (F1) & \(2\times\frac{precision\times recall}{precision+recall}\) & Harmonic mean between precision and recall. \\ \hline Accuracy (Acc.) & \(\frac{TP+TN}{TP+FP+TN+FN}\) & Evaluate the proportion of all the correct tests (TP and TN), over all the results obtained. \\ \hline Specificity (Spec.) & \(\frac{TN}{TN+FP}\) & Identifies if the method excludes nuclei without lesions correctly. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance metrics. (=3 \(\times\) 178) data. Finally, we balanced the normal and abnormal groups so that they had the same number of samples. In the CRIC database, there were 776 NILM data, 258 ASC-US, 539 LSIL, 483 ASC-H, 787 HSIL, and 70 SC. Thus, analogous to Herlev data balancing, to balance the CRIC data, we chose the reference size as 787, the most frequent class, HSIL. At the end of the balancing, each of the six classes was left with the same number of data. The 6-class balancing was also used for the 2- and 3-class balancing, aiming at the classes' representativeness within the groups. Thus, the abnormal group resulted in 3935 (=5 \(\times\) 787) data, and the normal one 787 (=1 \(\times\) 787). We employed a new balancing such that the two groups (normal and abnormal) were left with 3935 data. Finally, the high-grade lesion group resulted in 2361 (=3 \(\times\) 787) data, and the low-grade group in 1574 (=2 \(\times\) 787) data in the 3-class balancing.