## 4 Results and Discussion  
This section presents results and a discussion of the experimental results based on the DL approach. The DL approach has been shown to enhance the performance of GI disease classification tasks significantly. Table 1 depicts the model's performance and its parameters. The most common architectures [30; 31; 32] and baseline (ResNet50 = 90.28%, GoogLeNet = 91.38%, DenseNets = 91.60%, and baseline = 92.84%) demonstrated classification accuracy. Table 2 reveals cross-validation results in different five-folds, with reference to the size of the test dataset and other parameters. In Table 3, we compared the proposed method with other related methods [10; 23; 29]. The mean accuracy for ECA-Net equaled 92.81; for DL-OCT, it was 90.60; for LSTM-CNN, it was 93.10; for [PERSON] et al. it was 93; for our method, it was 93.19. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{3}{c}{**Evaluation Metrics**} \\ \cline{2-3}  & **Mean Accuracy (\%)** & **Parameters (Million)** \\ \hline ResNet50 [30] & 90.28 & 21.71 \\ GoogLeNet [31] & 91.38 & 5.61 \\ DenseNets [32] & 91.60 & 25.6 \\ Baseline (Ours) & 92.84 & 19.92 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Model performance complexity comparisons. Table 3 demonstrates a statistical comparison with other related methods. The comparison between the proposed method and other state-of-the-art methods was evaluated based on the same data and on standard evaluation metrics. Figure 6 demonstrates statistical comparisons of the DL method's performance in the confusion matrix. The confusion matrix calculates the actual and predicted rates for each disease category. The results of all four methods presented on the confusion matrix indicate disease classification performance. The test data ranges from 300 to 400 in each class. Overall performance accuracies of the respective methods were ECA-Net = 92.81; DL-OCT = 90.60; LSTM + CNN = 93.10; and Ours = 93.19. The ECA-Net method [23] revealed the GI disease or artifacts classification performance, with the pylorus classification of 100% and the lowest classification in esophagagitis (82%). The DL-OCT [10] method also demonstrated competitive classification accuracy, classifying pylorus at 98% and polyps at 81%. The LSMT-CNN [29] method exhibited competitive classification performance in pylorus (100%) and showed the lowest performance in the esophagagitis class (82%). The proposed method classified pylorus at 100% and showed the lowest performance in the esophagagitis disease classification (84%). Nearly all the methods classified pylorus best and misclassified the Z-line. Only a few data items were misclassified. Overall classification accuracy of all related methods was promising. The proposed method outperformed other methods significantly on GI disease or lesion classification by achieving a mean accuracy of 94.33%. Overall, the proposed method validated actual and predicted classifications better than other methods. It is essential to determine the location (anatomical and physiological) of the digestive system. Figure 7 confirms disease classification results using t-distributed stochastic neighbor embedding (t-SNE) visualization techniques, which show the transformation of high-dimensional features into low dimensions by preserving key features of endoscopic images. This technique provides better identification of each class in terms of depth and color. The proposed method, including other related works [10; 23; 29] demonstrated t-SNE-based classification performance. Our method achieved better qualitative results when compared with other works.