# Computer-Aided Bleeding Detection Algorithms for Capsule Endoscopy: A Systematic Review
## 5 Algorithm
The SVM classifier was applied to the clustering information to detect bleeding zones in CE images, obtaining a precision of 97.05%, False Positive Rate (FPR) of 1.1%, and False Negative Rate (FNR) of 22.38% [39]. **Deep Learning (DL):** The most widely used deep learning approach for image classification and segmentation is the Convolutional Neural Network (CNN). Several CNN models, such as AlexNet [95], LeNet [127], Fully Convolutional Neural Network (FCN) [27; 128], Visual Geometry Group Network (VGGNet) [129], Residual Network (Resnet-50) [130; 131; 132],Res2 Net101 [133], Inception-Resnet-V2 [134; 135], AttResU-Net [136], MobileNet [42], DenseNet [24], Region-based Convolutional Neural Networks (R-CNN) [137], Convolutional Recurrent Neural Network (CRNN) [34], U-Net [44; 138], SegNet [46], and custom CNNs [41; 139; 140; 141; 142; 143; 144; 145; 146; 147; 148; 149; 150; 151; 152; 153], have been utilized in a number of studies for the classification or segmentation or combined classification and segmentation of bleeding in CE images. The study in [27] presented an FCN model for an automatic blood region segmentation system. Another study [128] proposed a Look-Behind FCN algorithm for abnormality detection (polyps, ulcers, and blood) in CE images, achieving an accuracy of 97.84%. In [127], a LeNet model was trained and adopted pre-trained AlexNet, VGG-Net, and GoogLeNet models to identify intestinal hemorrhage. In [46], the authors applied a pre-trained AlexNet model for identification and a SegNet model for segmentation of intestinal bleeding. Another CNN algorithm, named U-Net, was proposed in [28; 44] to segment bleeding areas in CE images and videos. [PERSON] et al. [24] proposed a saliency-aware hybrid network algorithm based on two densely connected convolutional networks (DenseNets) for an automatic bleeding detection system. The authors of [85] developed a CNN model for detecting bleeding zones, which was trained using SegNet layers with bleeding, non-bleeding, and background classes. A blood content detection approach using ResNet architecture with 50 layers was suggested in [130], which achieved an accuracy of 99.89%, a sensitivity of 96.63%, and a specificity of 99.96%. [PERSON] et al. used a CNN model based on VGGNet to identify lesions with 96.83% accuracy [129]. Another CNN model for classifying bleeding in CE images was provided in another study [42]. The model was created utilizing MobileNet and a custom-built CNN. To identify small bowel angioectasia, the authors of [154] used a 16-layer Single Shot MultiBox Detector (SSD) deep CNN method. **Combined ML & DL algorithms:** In recent years, several researchers have employed CNN-based models, such as AlexNet [126], VGG [86], ResNet [121; 131], InceptionV3 [23], DenseNet [155], and XcepNet23 [81], to extract relevant features from medical images, particularly in tasks like identifying bleeding from normal images. In [86], deep CNNs (VGG16 and VGG19) were applied to extract features from CE images. A KNN algorithm was proposed to classify bleeding images, achieving 99.42% and 99.51% accuracy and precision rate, respectively. An automatic bleeding region segmentation technique was presented in [156] using individual MLP and CNN models. In another study [23], the authors proposed pre-trained deep CNNs (VGG19, InceptionV3, and ResNet50) models to extract bleeding features, and ML algorithms (SVM, KNN, Linear Regression) were utilized to distinguish bleeding and non-bleeding images. In [155], the authors applied DenseNet for feature extraction and the features were trained with an MLP algorithm to classify GI track abdominal infections. In addition, the study in [157] applied a CNN model to extract bleeding features and the SVM classifier was used to detect bleeding.