Rahim Tariq, Usman Muhammad Arslan, Shin Soo Young
Kumoh National Institute of Technology, Gumi, Gyeongbuk 39177, Republic of Korea.
Comput Med Imaging Graph. 2020 Oct;85:101767. doi: 10.1016/j.compmedimag.2020.101767. Epub 2020 Aug 28.
Wireless capsule endoscopy (WCE) is a process in which a patient swallows a camera-embedded pill-shaped device that passes through the gastrointestinal (GI) tract, captures and transmits images to an external receiver. WCE devices are considered as a replacement of conventional endoscopy methods which are usually painful and distressful for the patients. WCE devices produce over 60,000 images typically during their course of operation inside the GI tract. These images need to be examined by expert physicians who attempt to identify frames that contain inflammation/disease. It can be hectic for a physician to go through such a large number of frames, hence computer-aided detection methods are considered an efficient alternative. Various anomalies can take place in the GI tract of a human being but the most important and common ones and the aim of this survey are ulcers, polyps, and tumors. In this paper, we have presented a survey of contemporary computer-aided detection methods that take WCE images as input and classify those images in a diseased/abnormal or disease-free/normal image. We have considered methods that detect tumors, polyps and ulcers, as these three diseases lie in the same category. Furthermore, general abnormalities and bleeding inside the GI tract may be the symptoms of these diseases; so an attempt is also made to enlighten the research work done for abnormalities and bleeding detection inside WCE images. Several studies have been included with in-depth detail of their methodologies, findings, and conclusions. Also, we have attempted to classify these methods based on their technical aspects. A formal discussion and comparison of recent review articles are also provided to have a benchmark for the presented survey mentioning their limitations. This paper also includes a proposed classification approach where a cascade approach of neural networks is presented for the classification of tumor, polyp, and ulcer jointly along with data set specifications and results.
无线胶囊内镜检查（WCE）是一种让患者吞下一个嵌入摄像头的药丸状设备的过程，该设备穿过胃肠道（GI），捕捉图像并传输到外部接收器。WCE设备被视为传统内镜检查方法的替代品，传统方法通常会给患者带来痛苦和不适。WCE设备在胃肠道内运行过程中通常会产生超过60,000张图像。这些图像需要由专家医生进行检查，他们试图识别包含炎症/疾病的帧。医生查看如此大量的帧可能会很忙碌，因此计算机辅助检测方法被认为是一种有效的替代方法。人体胃肠道可能会出现各种异常情况，但本次调查的最重要且常见的异常情况以及目标是溃疡、息肉和肿瘤。在本文中，我们对当代计算机辅助检测方法进行了综述，这些方法将WCE图像作为输入，并将这些图像分类为患病/异常或无疾病/正常图像。我们考虑了检测肿瘤、息肉和溃疡的方法，因为这三种疾病属于同一类别。此外，胃肠道内的一般异常和出血可能是这些疾病的症状；因此，我们还尝试介绍针对WCE图像中异常和出血检测所做的研究工作。我们纳入了几项研究，并详细介绍了它们的方法、发现和结论。此外，我们还尝试根据其技术方面对这些方法进行分类。我们还对近期的综述文章进行了正式讨论和比较，以便为本次综述提供一个基准，并提及它们的局限性。本文还包括一种提议的分类方法，其中提出了一种神经网络级联方法，用于联合分类肿瘤、息肉和溃疡，同时给出了数据集规格和结果。
