# Feature Selection and Classification of Ulcerated Lesions Using Statistical Analysis for WCE Images
## 5 Conclusions  
This paper has outlined a new method for detecting ulcer in an entire GIT. This method utilizes the divide-and-conquers technique to extract ulcer frame from a complete video footage. Statistical analysis has been performed to achieve higher separation between ulcer and non-ulcerated pixels. This technique has sub-parts for computing feature vectors in order to reveal highly-relevant information for ulcer and non-ulcer pixel discrimination which includes image enhancement, transformation of various color space, statistic feature computation, and classification. Statistical analysis has been performed to achieve higher separation between ulcer and non-ulcerated pixels by using this technique. The pixels have been classified using seven color spaces. Instead of using any single color space, it is more important to extract band information using various color spaces in order to achieve more accurate results. In this proposed method, Cr, Y, and B bands have been selected from YCbCr, CMYK, and RGB color spaces, respectively. Additionally, overlapping area with correlation using grid search method has increased the performance in separating the ulcerated pixels from normal pixels. Grid method has been adopted as the searching procedure (0.8 step was used). Each \(\gamma\) and \(C\) value pair has been used in the training data with ten-fold cross-validation in order to evaluate the  
Figure 4: Performance of the proposed method.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Author** & **Color Space/Bands** & **Classifier** & **Dataset** & **Acc (\%)** & **Sen (\%)** & **Spe (\%)** \\ \hline
[31] & HSV & SVM & 137 images & 94.83 & 91.89 & 97.16 \\
[13] & RGB, HSV, CCV & MLP & 448 images & 86.93 & 89.03 & 85.56 \\
[32] & Various color space & Joint boost & 100 images & NA & 91.67 & 84.73 \\ Proposed & Cr, Y, B & WGS SVM (OACCorr) & 48,000 images & 97.89 & 96.22 & 95.09 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of performance with other author work.  
model performance. A large dataset has been used to create training and testing dataset in order to obtain meaningful result. In this work, the sensitivity and the specificity of ulcer classification using the grid search method in SVM are 96.22% and 95.09%, respectively, which are substantially higher than those of classification without grid search method (93.76% and 92.91%, respectively). The main contribution of this work is the implementation of a novel computer aided diagnostic method which can be used to discriminate ulcer pixel from non-ulcerated pixel. The method exhibits promising performance in terms of sensitivity, specificity, and accuracy. The current work has paved the way to providing a reliable computer-aided WCE diagnosis system.  
This research work is supported by the Centre for Intelligent Signal and Imaging Research (CISIR) and the Graduate Assistantship (GA) scheme, Universiti Teknologi PETRONAS, Perak, Malaysia.  
[PERSON] finished the draft of this paper. [PERSON] contributed in the design of the experimental work. [PERSON] contributed in feature extraction and feature selection algorithm for better discrimination between ulcer and foreground pixel. [PERSON] and [PERSON] contributed in the labelling of Ulcer dataset used in the experiment. [PERSON] and [PERSON] have contribution towards disease specification and other challenges found throughout the writing and other reviewing procedure. All authors reviewed and approved the contents of this paper.  
The authors declare no conflict of interest.