# Computer-Aided Bleeding Detection Algorithms for Capsule Endoscopy: A Systematic Review
## 5 Algorithm
In another study, a KNN classifier was employed [71] to distinguish the characteristics of bleeding and non-bleeding images. The classifier was trained with 200 color CE images and achieved 99.0% accuracy. **ANN or NN** is an ML algorithm for computer system designs that are inspired by biological neural networks. The approach described in [50] employed an ANN with 3 input neurons, 22 hidden neurons, and 2 output neurons, with a minimum squared error loss function. The ANN classifier was also applied in a preprocessing step to analyze the pixels in CE images in [25]. ANN is also known as NN. A NN cell classifier was applied in [76; 96] to categorize bleeding and non-bleeding patches in CE images. Another study proposed back-propagation NN to detect bleeding regions, achieving 97% sensitivity and 90% specificity. A Probabilistic Neural Network (PNN) is a radial basis function and Bayesian theory-based feedforward neural network. One such study [107] applied PNN to detect bleeding zones in CE images, achieving 93.1% sensitivity and 85.6% specificity. Multilayer Perceptron (MLP) is a type of fully connected feedforward ANN that is widely used in statistical pattern recognition. Several articles [32; 75; 80; 82; 83] employed MLP neural networks to classify bleeding images and regions in CE images. The article in [114] proposed the Vector Supported Convex Hull classification algorithm, which was compared to SVM and configured with two alternative feature selection approaches. The model achieved a 98% sensitivity and specificity ratio for bleeding detection. Another study [102] suggested a computer-aided color feature-based bleeding detection technique using a modified ant colony optimization algorithm. The model achieved 98.82%, 99.66%, and 98.01% accuracy, sensitivity, and specificity, respectively. **Other ML:** The Naive Bayes classifier is another ML classification algorithm based on the Bayes Theorem. The studies in [54; 100] used a Naive Bayes classifier to detect bleeding in CE images. In [38; 94], another ML algorithm, K-means clustering, was applied to extract important features for summarizing CE video clips. Random Tree and Random Forest are tree-based ML algorithms for making decisions. In the study in [78], a Random Tree classifier was trained with 100 bleeding and 100 non-bleeding images for a computer-aided bleeding detection system. The classifier achieved an accuracy of 99%, a sensitivity of 98%, and a specificity of 99%. In [79], both Random Tree and Random Forest classifier models outperformed bleeding detection compared to MLP and Naive Bayes models. Both models provided 99.5%, 99%, and 100% accuracy, sensitivity, and specificity, respectively. The Random Forest model was also used in [77], achieving 95.7% sensitivity and 92.3% specificity. In [65; 123], an Expectation Maximization (EM) clustering algorithm was used to detect potential bleeding regions as the ROI for subsequent classification of bleeding images from normal ones. **Combined multiple ML:** In [110], a block-based segmentation technique using local features was presented and several ML algorithms, like linear discriminant analysis, SVM, Random Forest, and ADABoost, were applied for discriminating between bleeding and non-bleeding images. Using the SVM and K-means algorithms, a GI bleeding detection approach was presented in [64] to detect bleeding images and regions of bleeding images, reporting less computation time with 98.04% accuracy and 84.88% precision. According to [72], an automatic detection system was designed to identify suspected blood indicators in CE images. The authors compared various ML classifiers, like SVM and NN. The ISVM classifier was trained with 136 normal with 214 abnormal images and achieved a maximum of 98.13% accuracy using the Total Margin-Based Adaptive Fuzzy (TAF-SVM) algorithm. In addition, an automatic bleeding detection approach for CE videos was suggested by [PERSON] et al. using a cluster-based feature.