# Computer-Aided Bleeding Detection Algorithms for Capsule Endoscopy: A Systematic Review
## 6 Discussion  
A CE device typically records video in the GI tract for around 8 h. Few studies utilized video to detect bleeding abnormalities. Figure 3 shows an overview of the used domains and algorithms in the papers that were reviewed in this study. To detect bleeding in CE images, researchers used three tasks, which included classification (C), segmentation (S), and combined classification and segmentation (C + S). Articles using the proposed classification task showed an average accuracy of 96.13% \(\pm\) 3.04, sensitivity of 95.13% \(\pm\) 4.25, and specificity of 95.63% \(\pm\) 4.03. For the segmentation algorithms, the average accuracy was around 94.95% \(\pm\) 4.11, sensitivity was 92.44% \(\pm\) 9.16, and specificity was 95.86% \(\pm\) 2.16. Articles that used the proposed combined task achieved an average accuracy of 97.12% \(\pm\) 1.95, sensitivity of 94.12% \(\pm\) 9.10, and specificity of 96.63% \(\pm\) 4.15. Based on the above literature analysis, the combined task performed better. One significant benefit of the current methods is their ability to identify bleeding in CE images/frames and pinpoint the specific bleeding region. However, a limitation of these methods is their inability to measure the extent or depth of the bleeding area. Feature extraction is an essential part of bleeding detection in CE images. The feature values are extracted from the color channels of CE images. The performance of a bleeding detection algorithm directly depends on the feature values. To identify bleeding, many color spaces were presented. RGB is the popular color space to extract features because it is the default color space. Apart from the RGB color space, several studies proposed individual color channels (R, G, B, etc. ), color channel pixel ratios (R/G, G/R, etc. ), or various color spaces (HSV, YIQ, YCbCr, CIE L*a*b*, CIE XYZ, K-L, etc.) to extract appropriate bleeding features. A few researchers applied two or more color spaces together to extract features. According to the taxonomy, all of the suggested color spaces were categorized into four groups: RGB, HSV, Combined (multiple color spaces), and Other (YIQ, YCbCr, CIE L*a*b*, CIE XYZ, etc.). It is important to acknowledge that the performance results presented in this article are directly extracted from the original papers. The box plots in all the figures were used to compare the performance between groups of methods, rather than between individual algorithms. Statistical measures such as the mean, median, 25 th percentile, and 75 th percentile were utilized for each group. The performance results of different color spaces in detecting bleeding using a box plot are shown in Figure 4. According to the figure, the choice of color space did not provide any performance benefits. All of the color spaces provided similar results except the 'Other' color space. When comparing all color spaces, the RGB color space had a slightly higher recall value. It should be noted that the recall performance criterion is the most important in the detection of bleeding. On top of that, the RGB color space achieved lower variance for accuracy, recall, and specificity. The current methods make a significant contribution by investigating all potential color spaces to detect bleeding in capsule endoscopy. Figure 3: Overview of all studies in this review. C = classification, S = segmentation, C + S = combined both classification and segmentation, ML = machine learning, DL = deep learning, ML + DL = both machine learning and deep learning. A typical approach for extracting bleeding features from whole CE images is the global feature extraction domain. Because this approach analyzes the entire image at once, the complexity and processing time are increased. The average results obtained using different extraction domains using a box plot are shown in Figure 5. To address the problem, various studies proposed a pixel-level feature extraction domain, in which the technique analyzed each pixel of the CE image.