## 2 Literature Review  
Identifying GI diseases is a prominent area of research within medical image analysis. Numerous studies approach this issue from various angles. For example, in [PERSON] et al. [7], a transformer neural network (TNU) with a spatial pooling configuration was used. The self-attention mechanism of a TUN enabled it to capture long-range information effectively, and the exploration of Vision Transformer's (ViT's) spatial structure through pooling had the potential to enhance further ViT's performance on a small-scale capsule endoscopy dataset. For capsule endoscopy disease classification, [PERSON] et al. trained the model entirely on two publicly available datasets and achieved good generalization effects with 79.15% accuracy on the multi-classification task of the Kvasir-Capsule dataset and 98.63% accuracy on the binary classification task of the Red Lesion Endoscopy dataset. In [PERSON] et al. [15], a hybrid CNN was developed to detect abnormalities by extracting a comprehensive set of meaningful features from wireless capsule endoscopy images using various convolution operations. The architecture consisted of three parallel CNNs, each with distinct feature learning capabilities. The first network employed depthwise separable convolution, while the second utilized cosine normalized convolution. The third network introduced an innovative meta-feature extraction mechanism to identify patterns from the statistical data derived from the features produced by the first two networks, as well as its own preceding layer. This combination of networks effectively managed intra-class variance and successfully identifies GI abnormalities. The proposed hybrid CNN model was trained and evaluated on two widely used publicly available datasets. The results indicated that the model outperformed six leading methods, achieving classification accuracies of 97% and 98% on the KID and Kvasir-Capsule datasets, respectively. In [PERSON] et al. [16], a method for dealing with a difficult dataset was developed using a convolutional neural network (CNN). With an accuracy of 97.82%, the proposed method could classify digestive tract abnormalities into 13 categories. The proposed approach's performance was enhanced when the authors utilized the appropriate optimizer and learning rates. The model's accuracy was also improved as a result of the convolution layer's filter modification. A more balanced dataset for training and higher-quality images could improve the model's accuracy. In [PERSON] et al. [17], to generate WCE images from existing WCE images, a WCE-DCGAN network was proposed. On SSD, YOLOv5, and YOLOv4, there were various degrees of performance improvement, achieving an average recognition accuracy of 97.25% on SSD with the images generated by this network and the original images as the input. In the meantime, the diversity of the images produced by WCE-DCGAN not only increased the size of the dataset but also gave the model a good generalization effect. Using WCE images and a deep CNN, [PERSON] et al. [18] proposed a method that was both reliable and effective for classifying GI tract anomalies. To accomplish this, the authors proposed the GI Disease-Detection Network (GIDD-Net) custom CNN architecture, which was built from the ground up and has relatively few parameters. Its goal was to detect disorders of the gastrointestinal tract with greater accuracy and efficiency at a lower computational cost. Additionally, by displaying class activation patterns in the bowels as a heat map, the proposed model successfully differentiated GI disorders. The authors in [18] used the synthetic over-sampling technique BORDERLINE SMOTE (BL-SMOTE) to evenly distribute the images among the classes to avoid the issue of class imbalance in the Kvasir-Capsule image dataset. The proposed model achieved the following values for evaluation metrics when compared to a variety of metrics: 98.8%, 98.9%, 98.9%, 99.8%, 0.0474, and 98.8% for precision, accuracy, F1-score, AUC, loss, and recall, respectively.