## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Folds**} & \multicolumn{4}{c}{**Evaluation Metrics**} \\ \cline{2-5}  & **Precision** & **Recall** & **F1-Score** & **Accuracy** \\ \hline Fold1 & 91.8 & 91.8 & 91.7 & 92.16 \\ Fold2 & 92.5 & 92.4 & 92.4 & 92.88 \\ Fold3 & 92.4 & 92.5 & 92.6 & 92.91 \\ Fold4 & 92.8 & 92.7 & 92.8 & 93.19 \\ Fold5 & 92.8 & 92.6 & 92.7 & 93.12 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Cross-validation statistical comparisons (mean values) on test datasets. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{4}{c}{**Evaluation Metrics**} \\ \cline{2-5}  & **Precision** & **Recall** & **F1-Score** & **Accuracy** \\ \hline ECA–Net [23] & 92.4 & 92.4 & 92.2 & 92.81 \\ DL–OCT [10] & 90.1 & 90.05 & 90.1 & 90.60 \\ LSMT–CNN [29] & 92.8 & 92.8 & 92.6 & 93.10 \\ [PERSON] et al. [5] & 92.7 & 92.6 & 92.7 & 93 \\ Ours & 92.8 & 92.7 & 92.8 & 93.19 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Statistical comparisons of related works (mean values). Figure 6: Confusion matrix of the GI disease classification on test datasets. The confusion matrix indicates the actual and predicted results of all ten classes: (**a**) The GI disease or artifacts classification performance of the ECA–Net [23], which classified pylorus better (100%), with the lowest classification (82%) achieved in esophagitis. (**b**) Classification accuracy of the DL–OCT [10] method’s performance. The method classified pylorus better (98%) and challenged the classification of polyps (81%). (**c**) Confirmed the competitive classification performance of the LSMT–CNN [29] method. The method better identified pylorus (100%) and showed the lowest classification results (82%) in the esophagitis class. (**d**) [PERSON] et al. [5] demonstrated better classification in pylorus. (**e**) The proposed method, like other methods [23, 29], classified pylorus better (100%), and the approach showed the lowest performance (84%) in the esophagitis disease classification. Figure 7: Validation of GI disease classification using the t-distributed stochastic neighbor embedding (t-SNE) visualization technique, which shows the transformation of high-dimensional features into low dimensions by preserving key features. The t-SNE technique provides better identification of each class in terms of depth-wise and color-wise approaches: (**a**) The ECA-Net [23] t-SNE method of classification performance and differentiates each class using multiple colors. (**b**) The DL-OCT [10] method’s t-SNE-based classification of each GI lesion category. (**c**) The LSMT-CNN [29] method’s t-SNE based classification results. (**d**) [PERSON] et al. [5] t-SNE projection results. (**e**) The proposed method’s t-SNE-based classification performance, which outperforms other related works significantly. In this study, t-SNE was applied to preserve the local structure of the data by a non-linear dimension reduction (DR) approach. A performance analysis was implemented to demonstrate the performance difference in the methods [33]. The t-SNE visualization showed significant improvement in terms of feature extraction and DR for GI disease classification. It preserved complex non-linear data structures by maintaining the local similarity structure of the data [27]. The t-SNE technique could efficiently project complex 2D endoscopic datasets. High-dimensional space was preserved as much as possible. Our t-SNE-based disease classification exhibited better feature extraction performance than other methods. The proposed method achieved better results in classifying GI diseases when compared with other recent related methods. However, the results of the proposed method come with some limitations. A fully supervised learning method needs a massive GI endoscopic image dataset, which is the method's main drawback. Another limitation of the proposed approach may be its lack of focus on pixel-wise classification and detection.