## 4 Results
### Classification Results  
We evaluated the performance of classification on a fine-tuned Xception model using different performance measures, namely, precision, recall, and accuracy. In medical applications, we are more concerned that recall should be high so that no disease case should be treated as normal. Precision and recall are already discussed in the segmentation, and their equations are also shown in (10) and (11); therefore, only accuracy is discussed in this section. Accuracy points out the number of true predictions from total predictions. The equation of accuracy is shown below:  
\[Accuracy=\frac{M+P}{M+N+O+P} \tag{12}\]  
where M is defined as true-positive, N is defined as false-positive, O is defined as false-negative, and P is defined as true-negative. Classification results were collected by distributing the dataset into various train-to-test ratios, namely, 80/20, 70/30, and 60/40, and using 10-fold cross-validation. Initially, results are collected based on input images with no contours using 10-fold cross-validation to compare the performance. It is evident from Table 2 that the softmax classifier outperforms other classifiers, with 89.62% precision, 78.25% recall, and 81.06% accuracy. However, quadratic SVM performance cannot be overlooked, as it is near to that of softmax. On an 80/20 ratio, the best achieved results using the softmax classifier are 87.67% precision, 80.13% recall, and 85.27% accuracy. Results achieved by applying multiple classifiers using our proposed model are shown in Table 3. It is evident from Table 3 that quadratic SVM performance is also satisfactory and near to that of softmax. Moreover, the testing accuracy graph based on the model trained for the 80/20 train-to-test ratio is also shown in Figure 8.  
The confusion matrix generated based on the 80/20 ratio using the softmax classifier is shown in Figure 9. Looking at the results, it is clear that in the ulcer class, four of the cases are shown as normal, and in the esophagitis class, three of the cases are treated as normal, which is an attentive sign. Moreover, six of the normal cases are treated as disease cases. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Precision** & **Recall** & **Accuracy** \\ \hline Softmax & **89.62\%** & **78.25\%** & **81.06\%** \\ \hline Linear SVM & 77.16\% & 71.33\% & 75.94\% \\ \hline Quadratic SVM & 88.97\% & 78.17\% & 80.68\% \\ \hline Bayesian & 77.16\% & 71.33\% & 75.94\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance matrices based on images without contours using 10-fold cross-validation. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Precision** & **Recall** & **Accuracy** \\ \hline Softmax & **87.67\%** & **80.13\%** & **85.27\%** \\ \hline Linear SVM & 81.92\% & 77.42\% & 79.19\% \\ \hline Quadratic SVM & 86.22\% & 80.10\% & 85.02\% \\ \hline Bayesian & 80.31\% & 74.22\% & 78.94\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance matrices based on the 80/20 train-to-test ratio. On a 70/30 ratio, the best results achieved using the softmax classifier are 96.94% precision, 93.22% recall, and 94.68% accuracy. Results achieved by applying multiple classifiers using our proposed model are shown in Table 4. It is evident from Table 4 that after softmax, Bayesian performance is better than the other classifiers. Moreover, the testing accuracy graph on the model trained with the 70/30 train-to-test ratio is also shown in Figure 10. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Precision** & **Recall** & **Accuracy** \\ \hline Softmax & **96.94\%** & **93.22\%** & **94.68\%** \\ \hline Linear SVM & 90.35\% & 87.01\% & 87.19\% \\ \hline Quadratic SVM & 91.77\% & 82.01\% & 86.22\% \\ \hline Bayesian & 91.64\% & 88.35\% & 88.34\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance matrices based on the 70/30 train-to-test ratio. Figure 8: Testing accuracy graph based on the 80/20 train-to-test ratio. Figure 9: Confusion matrix based on the 80/20 train-to-test ratio using softmax.