## 5 Classification
### Experiments  
We conducted a comprehensive set of experiments to assess the classification techniques (ANN and SVM classifiers). These different classifiers were applied to the sets of the input data composed of the extracted features (NC ratio, perimeter, density, curvature, circularity and Eigen ratio) from the sputum cells. Different performance criteria: sensitivity, specificity and accuracy have been computed for these classifiers. The first series was dedicated to the ANN classifier. Therefore, to validate the output results 10-fold, cross validation was used [27]. Tenfold cross validation is a resampling technique implemented by randomly choosing the training and testing data to evaluate the robustness of the ANN classifier. It will prevent the over-fitting that may occur if we divide the data into two sets only (training and testing sets). In 10-fold cross validation, the dataset (extracted features) was randomly divided into 10 blocks. For every hold out block, the system was trained on the remaining blocks and tested on the hold out block; results averaged over all test blocks, which reflects predictive performance. Initially, the best optimized ANN is obtained by varying various parameters of ANN, such as hidden nodes, number of epochs. Figure 12 shows the performance criteria of ANN obtained by applying 10-fold cross in the input data, where the \(x\)-axis represents the training and testing results and the \(y\)-axis represents the performance criteria which are: sensitivity, specificity and accuracy, in addition to displaying the misclassification error obtained in each training and testing fold in 10-fold cross validation. As can be seen from this figure, the overall accuracy reaches 90%. The next experiment, dedicated to the SVM classifier. The training and testing were done by using 10-fold cross validation. Figure 13 depicts the performance results for SVM. We tried many values for the kernel width (\(\sigma\)) (Equation (20)) and we found that \(\sigma=0.6\) gives the best performance in terms of the small number of support vectors and misclassification errors as illustrated in Table 5. We tried all combinations of two and three features to find the best feature subset that can separate the classes (benign and malignant) with better performance and less error, as well as less support vectors. Experimentally, we found that the geometric features (NC ratio, curvature and circularity) give us the best performance. Including additional features did not improve performance. Figure 12: Performance criteria of ANN with 10-fold cross validation. As can be seen from Figure 13, the value of FN and FP was reduced to its minimal, and the resulting values for the sensitivity and accuracy are large. Furthermore, we found that the SVM produced small misclassification errors in the training and testing phases. For qualitative purposes, Figure 14 illustrates the SVM result, by using NC ratio and curvature features respectively. As can be seen, there is a clear separation between the two classes (red points for benign and blue points for malignant) as represented by the black curve (margin) and the support vectors. Figure 15 illustrates a bad classification scenario, where the feature subset, which corresponds to the density (chromatic feature) and curvature (geometric feature), yields strong overlapping  
We found that the SVM outperforms the ANN classifiers. Indeed, the SVM classifier allows a clear separation and classification of the cells into benign and malignant classes. Therefore, it is more stable and reliable for the proposed CAD system. Table 6 summarizes the performances of the ANN and SVM classifiers, respectively. We found that the SVM classifier achieved the best scores. It obtains a high number of TP and TN, and reduced number of FP and FN which leads to successful classification where all the performance criteria measurements (sensitivity, specificity and accuracy) are increased and the classification error is highly reduced.