Department of Electrical and Electronic Engineering, Pabna University of Science and Technology, Pabna 6600, Bangladesh.
Faculty of Engineering and Technology, Multimedia University, Melaka 75450, Malaysia.
Sensors (Basel). 2023 Aug 14;23(16):7170. doi: 10.3390/s23167170.
Capsule endoscopy (CE) is a widely used medical imaging tool for the diagnosis of gastrointestinal tract abnormalities like bleeding. However, CE captures a huge number of image frames, constituting a time-consuming and tedious task for medical experts to manually inspect. To address this issue, researchers have focused on computer-aided bleeding detection systems to automatically identify bleeding in real time. This paper presents a systematic review of the available state-of-the-art computer-aided bleeding detection algorithms for capsule endoscopy. The review was carried out by searching five different repositories (Scopus, PubMed, IEEE Xplore, ACM Digital Library, and ScienceDirect) for all original publications on computer-aided bleeding detection published between 2001 and 2023. The Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) methodology was used to perform the review, and 147 full texts of scientific papers were reviewed. The contributions of this paper are: (I) a taxonomy for computer-aided bleeding detection algorithms for capsule endoscopy is identified; (II) the available state-of-the-art computer-aided bleeding detection algorithms, including various color spaces (RGB, HSV, etc.), feature extraction techniques, and classifiers, are discussed; and (III) the most effective algorithms for practical use are identified. Finally, the paper is concluded by providing future direction for computer-aided bleeding detection research.
胶囊内镜（CE）是一种广泛用于诊断胃肠道出血等异常的医学成像工具。然而，CE 会捕获大量的图像帧，这对于医学专家手动检查来说是一项耗时且乏味的任务。为了解决这个问题，研究人员专注于计算机辅助出血检测系统，以实现实时自动识别出血。本文对现有的用于胶囊内镜的计算机辅助出血检测算法进行了系统综述。该综述通过在五个不同的存储库（Scopus、PubMed、IEEE Xplore、ACM 数字图书馆和 ScienceDirect）中搜索 2001 年至 2023 年间发表的所有关于计算机辅助出血检测的原始出版物来进行。采用系统评价和荟萃分析的首选报告项目（PRISMA）方法进行综述，并对 147 篇科学论文的全文进行了回顾。本文的贡献如下：（I）确定了用于胶囊内镜的计算机辅助出血检测算法的分类法；（II）讨论了现有的最先进的计算机辅助出血检测算法，包括各种颜色空间（RGB、HSV 等）、特征提取技术和分类器；（III）确定了最适合实际应用的有效算法。最后，本文通过为计算机辅助出血检测研究提供未来方向来结束。
