## 4 Results
### Classification Results
The confusion matrix generated based on the 70/30 ratio using the softmax classifier is shown in Figure 11. Looking at the results, it is clear that while using this ratio, the model performs much better, and only one disease case is treated as normal, which is in the esophagitis class. Moreover, only four of the normal cases are treated as disease cases. On a 60/40 ratio, the best results obtained using the softmax classifier are 82.56% precision, 73.69% recall, and 78.06% accuracy. Results achieved by applying multiple classifiers using our proposed model are shown in Table 5. It is evident from Table 5 that quadratic SVM performance is also satisfactory and near to that of softmax. Moreover, the testing accuracy graph on the model trained with the 60/40 train-to-test ratio is shown in Figure 12.  
Figure 11: Confusion matrix based on the 70/30 train-to-test ratio using softmax. Figure 10: Testing accuracy graph based on the 70/30 train-to-test ratio. The confusion matrix generated based on the 60/40 ratio using the softmax classifier is shown in Figure 13. Looking at the results, it is clear that while using this ratio, model performance worsens, as three polyp cases, six ulcer cases, six ulcerative colitis cases, and ten cases of esophagitis were predicted as non-diseased. Moreover, it is also a great concern that 20 of the normal cases were treated as disease cases. We believe that the behavior of the model worsened as training data were reduced; hence, the model was not properly tuned. For 10-fold cross-validation, the best results achieved using the softmax classifier are 99.68% precision, 96.13% recall, and 98.32% accuracy. Results achieved by applying multiple classifiers using our proposed model are shown in Table 6. It is evident from  
Figure 8:0 Testing accuracy graph based on the 60/40 train-to-test ratio. Figure 8:1 Confusion matrix based on the 60/40 train-to-test ratio using softmax. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Precision** & **Recall** & **Accuracy** \\ \hline Softmax & **82.56\%** & **73.69\%** & **78.06\%** \\ \hline Linear SVM & 74.22\% & 67.42\% & 72.39\% \\ \hline Quadratic SVM & 82.18\% & 72.81\% & 77.83\% \\ \hline Bayesian & 78.16\% & 71.23\% & 77.81\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance matrices based on the 60/40 train-to-test ratio. Table 6 that the quadratic SVM as well as Bayesian performances are satisfactory, and cannot be ignored. Moreover, the testing accuracy graph based on the model trained with 10-fold cross-validation is depicted in Figure 14.  
The confusion matrix produced by 10-fold cross-validation with the softmax classifier is shown in Figure 15. Looking at the results, it is clear that the model performs significantly better, and no disease case is treated as normal, which is the prime focus in medical applications. Hence, we achieved our desired performance using the proposed methodology. Moreover, only one of the normal cases is treated as a disease case. After analyzing the results, it is understandable that better results are achieved with 10-fold cross-validation, which shows the reliability of our model; hence, we select it as our proposed model. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Precision** & **Recall** & **Accuracy** \\ \hline Softmax & **99.68\%** & **96.13\%** & **98.32\%** \\ \hline Linear SVM & 91.72\% & 89.29\% & 90.07\% \\ \hline Quadratic SVM & 99.24\% & 95.04\% & 97.64\% \\ \hline Bayesian & 97.63\% & 94.46\% & 97.28\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance matrices based on 10-fold cross-validation. Figure 14: Testing accuracy graph based on 10-fold cross-validation. Figure 15: Confusion matrix based on 10-fold cross-validation using softmax.