# Computer-Aided Bleeding Detection Algorithms for Capsule Endoscopy: A Systematic Review
###### Abstract
To discriminate the pathology, both texture and color features are used. Since the CE diagnostic process captures over 57,000 images, manual reviewing is a labor-intensive and time-consuming task for physicians in order to detect bleeding regions [9], and it may involve several challenges due to complex backgrounds, low contrast, variations in the lesion, and color. This may affect the accuracy of subsequent classification and segmentation [10; 11]. These issues complicate objective disease diagnosis and necessitate the opinions of many specialists to avoid misdiagnosis. As a result, there is a strong need for an alternate technique to automatically detect bleeding in the GI tract. Some research has been conducted on the automated inspection and analysis of CE images. Software suites that use computational techniques are often made available with the brand of a particular capsule and are used by a lot of people. The benefits include the efficiency and availability of a tool that can automatically detect bleeding regions and improve diagnostic accuracy. Commercial software built by Given Imaging aims to recognize spontaneous active blood, although the reported sensitivity and specificity are not satisfactory [8]. Although CE has many advantages, research in CE technology is not widespread. For instance, at present, it is challenging for physicians to go through the entire collection of more than 50,000 frames in order to diagnose a disease. Due to visual fatigue and the relatively small size of the lesion region, the disease may go undetected in its early stages. The fact that software packages already available on the market are based on low-level, hand-crafted feature extraction algorithms that have poor generalizability should not be ignored. Additionally, because the feature extraction and classification phases are separated in hand-crafted feature-based techniques, it is difficult to make reliable diagnostic decisions. Several informative original articles and reviews on bleeding detection in CE images have been published over the last 15 years. The authors of [12] reviewed the clinical applications and developments of small bowel CE, i.e., small bowel tumors, Celiac disease, and [PERSON]'s disease. They gave insight into the potential future prospects of small bowel CE. In [13], the authors discussed different imaging methods, including signal processing, color and image processing, and artificial intelligence, for representing, analyzing, and evaluating CE images. In [14], the authors calculated performance metrics (accuracy, positive and negative predictive values, sensitivity, specificity) and compared the diagnostic accuracy of video CE and double-balloon enteroscopy in cases of obscure GI bleeding of vascular origin. Another study [15] discussed the market's available CE models, including diagnostic yield, safety profile, image quality, and technical evolution, for small bowel CE. That study analyzed five commercially available types of small bowel capsule endoscopes, which were the PillCam(r)SB2, MiroCam(r)v2, EndoCapsule(r), OMOM(r) (SmartCapsule), and CapsoCam(r)SV1. In [16], the authors reviewed and analyzed the literature for computational methods that could be applied in software to improve the diagnostic yield of video CE. Another research group [17] reviewed a deep learning-based approach (CNN) for CE, which was used to solve a variety of issues, e.g., detection of polyp/ulcer/cancer, bleeding/hemorrhage/angiectasia, and hookworms. In [18], the authors reviewed all of the image features (color, texture, shape) for image abstraction in machine vision-based analysis of CE videos and reviewed computer-aided diagnostic systems of CE images. The authors mainly concentrated on the study of shot boundaries and GI pathology detection. In the literature, the authors of [19] assessed the accuracy of video CE to identify active hemorrhage in the upper GI.