# Feature Selection and Classification of Ulcerated Lesions Using Statistical Analysis for WCE Images
## 4 Experimental Results and Discussions
### Performance Metrics  
Performance metrics such as accuracy (Acc), sensitivity (Sen), and specificity (Spe) were computed to evaluate the effectiveness of the proposed method. For the experimentation on GIT image data set, the positive sample represents lesion images and the negative sample represents normal images. The equations for sensitivity and specificity can be expressed as:  
\[\text{Accuracy (Acc)}=\text{TP}+\text{TN}/(\text{TP}+\text{TN}+\text{FP}+ \text{FN}) \tag{4}\]  
\[\text{Sensitivity (Sen)}=\text{TP}/(\text{TP}+\text{FN}) \tag{5}\]  
\[\text{Specificity (Spe)}=\text{TN}/(\text{FP}+\text{TN}) \tag{6}\]  
where TP represents the number of true positives, TN represents the number of true negatives, FP represents the incorrect positive image samples identified and FN represents the incorrect negative image samples identified. From the clinical experts, sensitivity defines the probability of a positive analysis and specificity is the probability of a negative analysis.  
In Table 2, SVM (OA) is depicted as Support vector machine for overlapping area. It is computed for seven bands, six bands, and five bands and the results are shown in Table 2. Similarly, SVM (OACCorr) is a support vector machine for overlapping area with cross-correlation value and it is computed for four bands, three bands, and two bands. Here, \(Cr\), \(Y\), \(A\), \(H\) and \(B\) give better results for Method 1 (OA). On the other hand, \(Cr\), \(Y\) and \(B\) give the best result with Method 2 (OACCorr). Specifically, Method 2 is more promising than Method 1. The above results shows that combining various color bands can provide more meaningful results.  
Figure 4 shows the analyses of SVM (OACCorr) by using grid search method (WGS) and without grid search method (WOGS). The accuracies of 3 datasets have been compared. From the results taken from \(Cr\), \(Y\), \(B\) color bands, the extracted feature is more obvious using grid search method. For this method, the average accuracy, sensitivity, and specificity are 97.89%, 96.22% and 95.09%, respectively.  
Additionally, we have compared our results of classification with others as presented in Table 3. It is important to note the dataset provided by other authors might be extracted from different capsule. Therefore, for comparison purpose, the dataset employed in the current work has been used for all  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Methods**} & \multicolumn{6}{c}{**Color Bands**} \\ \hline \multirow{4}{*}{**Method 1**} & \multirow{4}{*}{SVM (OA)} & \multicolumn{2}{c}{\(Cr\), \(Y\), \(A\), \(H\), \(B\), \(U\)} & \(Cr\), \(Y\), \(A\), \(H\), \(B\) & \multicolumn{2}{c}{\(Cr\), \(Y\), \(A\), \(H\)} \\ \cline{3-8}  & & Sen & Spe & Sen & Spe & Sen & Spe \\  & 90.32 & 90.55 & 91.98 & 91.56 & 91.58 & 91.23 \\ \hline \multirow{4}{*}{**Method 2**} & \multirow{4}{*}{SVM (OACCorr)} & \multicolumn{2}{c}{\(Cr\), \(Y\), \(B\), \(S\)} & \multicolumn{2}{c}{\(Cr\), \(Y\), \(B\)} & \multicolumn{2}{c}{\(Cr\), \(Y\)} \\ \cline{3-8}  & & Sen & Spe & Sen & Spe & Sen & Spe \\ \cline{1-1} \cline{2-8}  & 92.58 & 91.84 & 93.76 & 92.91 & 93.08 & 91.36 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Sensitivity, specificity, and accuracy using SVM classifier.  
methods, including those reported by other researchers (Table 3 shows the dataset used by other authors). The authors in [31] combined the merits of both Contourlet transform and Log Gabor filter in HSV color space; however, their dataset was very small. Author from [13] utilized RGB and HSV color space and classification was performed using MLP neural network. By using color coherence vector (CCV), a promising result was attained. As reported in [32], color wavelet covariance feature was used on various color spaces, and Texton boost was applied to classify normal and abnormal tissues. It is interesting to note that the proposed method shows higher sensitivity and higher specificity even for a very large dataset.