# Computer-Aided Bleeding Detection Algorithms for Capsule Endoscopy: A Systematic Review
## 6 Discussion
The technique improved the results but did not reduce the complexity and computation time. Several authors selected a portion of the CE image (specific block size, ROI, POI) in the preprocessing step for feature extraction, named the local feature extraction domain. The technique improved the results and reduced the complexity and processing time. Recently, a few researchers applied both the global and local feature extraction domains in a computer-aided bleeding detection system, which significantly enhanced the detection accuracy compared to the individual domains. Texture and statistical values (mean, mode, variance, moment, entropy, energy, skewness, kurtosis, etc.) were calculated using the feature values. Finally, a classification or segmentation algorithm was used to extract the values in order to detect bleeding in the CE images. According to Figure 5, the combined feature extraction domain outperformed the other domains in terms of accuracy, sensitivity, and specificity because it was tested at both the pixel and image levels. Also, a CNN model was used to train the model at the pixel level, followed by the application of a classification model to detect bleeding. The current feature extraction methods have certain limitations, such as introducing bias (which is influenced by the chosen algorithm), increased complexity, overfitting, and reduced generalizability. The majority of the literature reviewed proposed various ML algorithms that were trained with the texture and statistical features to identify bleeding in the CE images. Before introducing advanced ML algorithms, researchers set threshold values for the extraction features to detect bleeding in the CE images. The review of the literature found that the greatest number of articles used the KNN, SVM, MLP, and NN algorithms. In addition to these algorithms, a few other ML algorithms were used, such as Principal Component Analysis (PCA), Random Tree, Random Forest, Fuzzy C-Means, Expectation Maximization clustering, and Vector Supported Convex Hull, which were called \"Other ML\" in this study. The performance of the ML technique is based on the extracted features of color channels. The color channel intensity values overlapped between bleeding and non-bleeding pixels. As a result, utilizing ML approaches to distinguish bleeding from non-bleeding in CE images is problematic. In the last few years, researchers have proposed a DL technique,  
Figure 4: Performance of different color spaces. (‘X’ and circle represent mean and outlier, respectively). particularly using CNNs (AlexNet, LeNet, FCN, VGGNet, ResNet-50, MobileNet, U-Net, DenseNet, and SegNet) to identify bleeding in CE images. DL is an end-to-end classification and segmentation approach that extracts features automatically at the pixel level. Unlike ML, the DL approach does not require a separate feature extraction stage and it extracts features automatically to provide more efficient outcomes. The performance results for different state-of-the-art ML and DL algorithms using a box plot are shown in Figure 6. From the figure, it is observed that both the KNN and CNN algorithms outperformed the other algorithms. While the existing methods have made significant contributions to the development of classification and segmentation algorithms for detecting bleeding with satisfactory performance, they have often been tested on a limited number of test samples, such as images. Furthermore, deep learning algorithms have not yet incorporated attention mechanisms to further enhance their performance. We have added Appendix A at the end of our paper, which includes all of the information gathered from the reviewed articles in Table A1. We compared the performance of the research methods of the reviewed articles using established metrics such as accuracy, recall, specificity, Dice score, F1 score, Intersection over Union (IoU), etc. Figure 5: Performance of different feature extraction domains. (‘X’ and circle represent mean and outlier, respectively). Figure 6: Performance of different state-of-the-art algorithms.