## 4 Model Implementation and Evaluation
### Model Implementation  
We conducted three experiments using the Kvasir-Capsule and Kvasir v2 datasets. In the first experiment, we integrated the ILRC process with the EfficientNet-B0, ResNet101v2, InceptionV3, and InceptionResNetV2 DL models. This integration allows for the adaptive adjustment of the LR based on the training progress, which enhances convergence speed and reduces the risk of overfitting. We again evaluated the four DL models on the Kvasir v2 dataset after applying data augmentation techniques. In the second and third experiments, we continued to combine the ILRC process with the four DL models. In the second experiment, we did not use under-sampling or data augmentation techniques on the Kvasir-Capsule dataset. However, in the third experiment, we applied both under-sampling and data augmentation techniques on the same dataset. For our experiments, the Kvasir-Capsule and Kvasir datasets were split into 70% for training, 15% for testing, and 15% for validation. All three experiments were performed in the Kaggle environment. Table 6 lists the specifications of the computer used in our experiments. **In the first experiment, we applied the data augmentation technique to the balanced Kvasir v2 dataset, and we integrated the ILRC process with the EfficientNet-B0, ResNet101v2, InceptionV3, and InceptionResNetV2 DL models. This integration allows for the adaptive adjustment of the LR based on the training progress, which enhances convergence speed and reduces the risk of overfitting. The Kvasir v2 dataset contains 8000 images classified as eight different classes, namely, esophagitis, dyed lifted polyps, dyed resection margins, normal pylorus, normal z-line, normal, polyps, and ulcerative colitis. **  
Table 7 shows the results of the first experiment conducted with EfficientNet-B0, ResNet101V2, InceptionV3, and InceptionResNetV2. For multi-classification, we used the four CNN models on the Kvasir v2 dataset to distinguish images from eight classes. According to Table 7, the average accuracies for the models EfficientNet-B0, ResNet101, ResNet101V2, InceptionV3, and InceptionResNetV2 were 97.770%, 97.375%, 98%, and 98.062%, respectively. Among these models, InceptionResNetV2 achieved the highest averages for accuracy, recall, precision, and the F1-score, with values of 98.062%, 92.284%, 92.290%, and 92.242%, respectively. On the other hand, the ResNet101V2 model had the lowest averages for these metrics, recording 97.375% for accuracy, 89.506% for recall, 89.623% for precision, and 89.471% for the F1-score. Figure 4 shows the training and validation loss for each epoch of both the training and validation sets across four DL models that utilize the ILRC algorithm. For EfficientNet-B0, between 0 and 2.5 epochs, both the training and validation losses begin high and decrease quickly, which is typical as the model starts to learn patterns from the data. From 2.5 to 7.5 epochs, the validation loss experiences some fluctuations with noticeable spikes, while the training loss continues to decrease steadily. These spikes in the validation loss indicate that the model may be struggling to generalize to unseen data, a common occurrence in the early phases of training. This suggests that the model might be starting to overfit the training data slightly. However, these fluctuations stabilize as training progresses. From 7.5 \begin{table}
\begin{tabular}{c c} \hline
**Item** & **Description** \\ \hline CPU & Intel(R) Xeon(R) CPU @ 2.20 GHz \\ GPU & NVIDIA Tesla P100 with 16 GB of memory \\ RAM & 32 GB of memory \\ OS & Ubuntu 2024 x86\_64 GNU/Linux \\ \hline \end{tabular}
\end{table} 
Table 6: The used PC’s description. \begin{table}
\begin{tabular}{c c c c c} \hline
**Model** & **Accuracy (\%)** & **Recall (\%)** & **Precision (\%)** & **F1-Score (\%)** \\ \hline EfficientNet-B0 & 97.770 & 91.071 & 91.107 & 91.061 \\ ResNet101V2 & 97.375 & 89.506 & 89.623 & 89.471 \\ InceptionV3 & 98 & 92.004 & 92.071 & 92 \\ InceptionResNetV2 & 98.062 & 92.284 & 92.290 & 92.242 \\ \hline \end{tabular}
\end{table}