## 5 Conclusions  
In this study, we presented the ILRC mechanism aimed at enhancing the training efficiency of DL models. The ILRC dynamically modifies the LR in response to the training process's advancements, which accelerates convergence and minimizes the risk of overfitting. We evaluated this mechanism across various DL architectures, including EfficientNet-B0, ResNet101v2, InceptionV3, InceptionResNetV2, and Xception. Additionally, we improved the model layers by combining traditional transfer learning with freezing layers, fine-tuning strategies, residual learning, and contemporary regularization techniques. These models were fine-tuned using the Kvasir-Capsule and KVASIR v2 datasets, which comprise images sourced from WCE. To address the class imbalance within the Kvasir-Capsule dataset, we employed an under-sampling method to achieve a balanced distribution of images among the classes. Furthermore, we utilized a data augmentation strategy (over-sampling) to create additional WCE images from existing samples, effectively enlarging both datasets. This process involved altering four attributes of the existing WCE images. We also resized the images in the two datasets to 336 \(\times\) 336 pixels and normalized their contrast. The models--EfficientNet-B0, ResNet101v2, InceptionV3, InceptionResNetV2, and Xception--demonstrated remarkable performance compared to existing benchmarks, at \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Methodology** & **Accuracy** & **Dataset** \\ \hline \multirow{3}{*}{[PERSON]. [PERSON] et al. [7]} & \multicolumn{3}{c}{79.15\% on multi-classification} \\  & TNU and ViT & \begin{tabular}{c} and 98.63\% on binary \\ classification \\ \end{tabular} & \begin{tabular}{c} Kvasir-Capsule image and \\ Red Lesion Endoscopy \\ \end{tabular} \\  & \begin{tabular}{c} Hybrid CNNs \\ CNN \\ \end{tabular} & 98\% & \begin{tabular}{c} Kvasir-Capsule image \\ Kvasir-Capsule image \\ Image \\ \end{tabular} \\  & \begin{tabular}{c} [PERSON] et al. [17] \\ \end{tabular} & \begin{tabular}{c} WCE-DCGAN \\ \end{tabular} & 97.25\% & \begin{tabular}{c} Kvasir-Capsule image \\ Kvasir-Capsule image \\ Image \\ \end{tabular} \\  & \begin{tabular}{c} GIDD-Net and BL-SMOTE \\ \end{tabular} & 98.9\% & \begin{tabular}{c} Kvasir-Capsule image \\ Kvasir-Capsule image \\ Image \\ \end{tabular} \\  & \begin{tabular}{c} KNNs \\ AlexNet \\ Segmentation, feature \\ descriptor \\ (CLBP + PHOG + Bag of \\ Words (BoW)) \\ \end{tabular} & 97.86\% & \begin{tabular}{c} Kvasir-Capsule image and \\ Medical Image Computing \\ and Computer-Assisted \\ Intervention (MICCAI) 2017 \\ \end{tabular} \\  & \
\begin{tabular}{c} DenseNet201, InceptionV3, \\ and ResNet50 \\ EfficientNet-B0, ResNet101v2, \\ InceptionV3, \\ InceptionResNetV2, and \\ Xception utilizing the ILRC \\ \end{tabular} & 95\% &
\begin{tabular}{c} Kvasir v2 \\ **99.906\% and 98.0625\%** \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 13: A comparison of the proposed model and state-of-the-art methods. taining accuracies of 99.90%, 98.765%, 98.312%, 99.344%, and 99.719%, respectively, on the Kvasir-Capsule dataset. On the Kvasir-v2 dataset, they achieved accuracies of 97.770%, 97.375%, 98%, 98.0625%, and 98.062%, respectively. The combination of the ILRC with traditional transfer learning, freezing layers, fine-tuning methods, residual learning, and modern regularization techniques in our proposed models provides a strong solution for automating the detection of gastrointestinal abnormalities in WCE images, thereby enhancing diagnostic efficiency and accuracy in clinical practice. The limitations of our proposed model are related to the training and testing phases. The structure of the model contributed to this issue. Therefore, we plan to simplify our model in future work to enhance the speed of the training and testing phases. Additionally, we will test the proposed model on different types of human diseases and use hyper-optimization algorithms to automatically improve hyper-parameterization.