## 2 Literature Review
In [PERSON] [13], WCE images were analyzed in normalized RGB color space to investigate bleeding because different shades of red are associated with the human perception of bleeding. An effective region of interest (ROI) was first extracted from the WCE image frame using the interplane intensity variation profile in normalized RGB space in the proposed method. The variation in the normalized green plane from the extracted ROI was then displayed using a histogram. The suggested normalized green plane histograms were used to extract features. The K-nearest neighbor (KNN) classifier was used for classification purposes. Furthermore, morphological operations were used to extract bleeding zones from a bleeding image. A tenfold cross-validation scheme used 2300 WCE images from 30 publicly available WCE videos for performance evaluation. The proposed method outperformed the four reported existing methods with an accuracy of 97.86%, a sensitivity of 95.20%, and a specificity of 98.32%. [PERSON] et al. [19] found small intestinal ulcers and erosion in WCE images by proposing a novel computer-aided detection (CAD) approach based on a deep learning framework. The compression and cropping of images were included in the developed method. The authors trained the AlexNet convolutional neural network on a database of tens of thousands of WCE images to distinguish between the lesion and normal tissue. The detection of ulcers and erosions yielded results with high specificities of 94.79% and 95.98%, a high sensitivity of 96.80%, and a high accuracy of 95.34%, respectively. In both networks, the area under the receiver operating characteristic curve was greater than 0.98. [PERSON] et al. [20] proposed an approach to ulcer recognition and detection in WCE images. The authors pre-processed the input WCE images in the first step. Then, they suggested combining color and texture saliency maps by thresholding the resulting map and segmenting it. They discovered that CLBP, PHOG, and BoW produced the best results for feature extraction. A new classification method employing conventional classifiers and the HMM model was fed these features. The proposed method was effective at detecting ulcerous images because it significantly improved classification results with an accuracy of 94.8%. In [PERSON] et al. [21], a bleeding detection system utilizing deep transfer learning has been proposed. To extract features, three well-known CNN models--InceptionV3, VGG19, and ResNet50--were employed. Feature selection was performed using the minimum redundancy maximum relevance method. Ultimately, supervised ML methods were applied to classify the selected features into two categories: non-bleeding images and bleeding images. The proposed architecture demonstrated exceptional performance, achieving average accuracies of 97.65% and 95.70% on leading datasets, surpassing the performance of single DL architectures. The optimal combination of accuracy and training time was attained by employing mean value pooling as a fusion rule and SVM as the classifier. [PERSON] et al. [22] introduced an ensemble model that utilized the predictions from three pre-trained models, DenseNet201, InceptionV3, and ResNet50, which achieved accuracies of 94.54%, 88.38%, and 90.58%, respectively. The predictions from these foundational models were merged using two techniques: model averaging and weighted averaging. After evaluating the model performances on the Kvasir v2 dataset, the authors found that the model averaging ensemble achieved an accuracy of 92.96%, while the weighted average ensemble reached an accuracy of 95.00%. All the studies mentioned above have the following limitations:  
* All the research mentioned above utilized a static LR. * Training on a small dataset may not provide a comprehensive representation of all possible abnormalities in the digestive system. This can limit the model's ability to generalize to new unseen cases.