# Computer-Aided Bleeding Detection Algorithms for Capsule Endoscopy: A Systematic Review
## 5 Algorithm  
Initially, researchers proposed various threshold values to detect bleeding [51; 52]. Various machine learning (ML) and deep learning (DL) algorithms are currently being used for accurate bleeding detection. **Machine Learning (ML):** Several ML algorithms have been applied in computer-aided bleeding detection systems to effectively detect bleeding in CE images or videos, such as SVM, KNN, K-Means Clustering, Naive Bayes, Random Tree, Random Forest, ANN, PNN, MLP, etc. **SVM** is one of the most popular supervised ML algorithms that is used to detect bleeding and non-bleeding images or zones from CE images or videos. The majority of studies used SVM based on the extracted features of input images including color space and texture [119; 122]. In 2008, [PERSON] et al. [48] developed an automated obscure bleeding detection technique for the GI tract that could classify bleeding and non-bleeding CE images using the SVM algorithm. An automated bleeding detection approach was presented in [47] that provided an accuracy of 97.67%. Another research group suggested an automatic bleeding image detection technique utilizing an SVM classifier to detect bleeding and non-bleeding frames from CE videos. The approach reported 94.50% accuracy, 93.00% sensitivity, and 94.88% specificity [53]. The study in [36] utilized the SVM classifier to train with 200 bleeding and 200 non-bleeding CE images and achieved 97.96%, 97.75%, and 97.99% accuracy, sensitivity, and specificity, respectively. Studies in [49; 124] suggested a system to automatically detect bleeding in CE images using the SVM classifier. A recent study suggested a Quadratic Support Vector Machine (QSVM) classifier for an automated bleeding detection approach, which was proposed in [1]. A fuzzy logic technique was applied to extract the features of the images. The model achieved 98.2%, 98%, and 98% accuracy, sensitivity, and specificity, respectively. [PERSON] et al. [125] presented an SVM classification model based on an improved Bag of Visual Words to detect bleeding in CE images. Different kernel functions of the SVM algorithm, including linear, polynomial (cubic [126], quadratic [81]), and Radial Basis Function (RBF) [120] were used in bleeding detection in CE image research. The SVM classifier with the linear kernel was utilized in a real-time computerized gastrointestinal hemorrhage detection method for CE videos. The results obtained 99.19%, 99.41%, and 98.95% accuracy, sensitivity, and specificity, respectively [118]. [PERSON] et al. [60] presented an automatic detection gastric hemorrhage system using the SVM classifier with RBF as the kernel function, which achieved 95.8% accuracy, 87.5% sensitivity, 98.1% specificity, 12.5% miss detection rate, and 1.9% false detection rate, respectively. Another study [21] also used the RBF kernel of the SVM classifier to discriminate between bleeding and non-bleeding images, achieving 98.0%, 97.0%, and 98.0% accuracy, specificity, and sensitivity, respectively. In addition [105], the SVM classifier model was used to detect bleeding using the chi-square kernel and histogram intersection. The combination of spatial pyramids with a robust hue histogram improved the accuracy by about 8%. **KNN** is the second most popular supervised ML algorithm to detect bleeding in CE images or videos. The algorithm processes all existing pixels of CE images and classifies new pixels based on similarities. In [90], the KNN classifier was used to train with CE videos, achieving 97.50% accuracy, 94.33% sensitivity, and 98.21% specificity. [PERSON] et al. [62] employed a KNN model for detecting bleeding in CE images, which achieved an accuracy of 98.12%, a sensitivity of 94.98%, and a specificity of 98.55%. The article in [103] presented a bleeding detection approach for CE images that was compared to various ML algorithms, such as SVM, AdaBoost (Adaptive Boosting), and KNN, and the KNNalgorithm achieved the best results with 99.22% accuracy.