# Computer-Assisted Screening for Cervical Cancer Using Digital Image Processing of Pap smear Images
## 3 Results  
To validate the effectiveness of our proposed system, SIPaKMeD (multi-cells) dataset and Herlev dataset (single-cell) were used. In the multi-cells dataset, there were 996 images, and 4049 cells were cropped from these total images. These cells were divided into five classes, class1 (superficial intermediate cells), class 2 (parabasal cells), class 3 (metaplastic cells), class 4 (dyskeratotic cells), and class 5 (koilocytotic cells). For the two-class problem, the first three classes were grouped into normal cells and 1618 cells in total. The last two classes were grouped into abnormal cells and 2431 cells in total. The performance measures were accuracy, recall, specificity, precision, and F-measure [39]. The formulas are given in the below equations:  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{FP}+\text{TN}+ \text{FN}} \tag{1}\]  
\[\text{Sensitivity}\backslash\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{ FN}} \tag{2}\]  
\[\text{specificity}=\frac{\text{TN}}{\text{TN}+\text{FP}} \tag{3}\]  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{4}\]  
\[\text{F\_Measure}=2*\frac{\text{Precision}*\text{Recall}}{\text{Precision}+ \text{Recall}} \tag{5}\]  
where True positive (TP)--the results of the correctly classified positive class. True negative (TN)--the results of the correctly classified negative class. False-positive (FP)--the results of the incorrectly classified positive class. False-negative (FN)--the results of the incorrectly classified negative class. The comparison results of classification performance in terms of accuracy with five classifiers and our ensemble classifier using nuclei features only, cytoplasm features only, combining nuclei and cytoplasm features without features selection method, and with features selection method for the two-class problem (SIPaKMed dataset) are shown in Figure 14 and five-class problem in Figure 15.  
Figure 13: Block diagram of ensemble classifiers. (where C1 = LD, C2 = SVM, C3 = KNN, C4 = Boosted trees, and C5 = Bagged trees). LN, linear discriminant; SVM, support vector machine; KNN, k-nearest neighbor. Moreover, the five performance measures of each classifier using selected features are shown in Figure 16 for the two-class problem and Figure 17 for the five-class problem. Figure 14: Classification performance of five classifiers and our ensemble classifier in terms of accuracy using four datasets (nuclei features only (Accuracy_N), cytoplasm features only (Accuracy_C), combining nuclei and cytoplasm features without features selection method (Accuracy_NC), and with features selection method (Accuracy_Selected)). (SIPaKMed dataset for the two-class problem). Figure 15: Classification performance of five classifiers and our ensemble classifier in terms of accuracy using four datasets (nuclei features only (Accuracy_N), cytoplasm features only (Accuracy_C), combining nuclei and cytoplasm features without features selection method (Accuracy_NC), and with features selection method (Accuracy_Selected)). (SIPaKMed dataset for the five-class problem). The comparison results of five classifiers with our ensemble classifier in terms of accuracy using four datasets (nuclei features only, cytoplasm features only, combining nuclei and cytoplasm features without features selection method, and with features selection method) are shown in Figure 18 for the two-class problem and Figure 19 for the seven-class problem. Figure 16: Evaluation of classifier’s performance using selected features for the SIPAKMed dataset in the two-class problem. \[\text{Sensitivity}\backslash\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{ FN}} \tag{2}\]0 Evaluation of classifier’s performance using selected features for the SIPAKMed dataset in the five-class problem. The five performance measures of each classifier using selected features for the Herlev dataset are shown in Figure 20 for the two-class problem and Figure 21 for the seven-class problem.