Wireless capsule endoscopy (WCE) is a device that can move through human body and capture the small bowel entirely. Thus, it is presented as an excellent diagnostic tool for evaluation of gastrointestinal diseases compared with traditional endoscopies. However, the diagnosis by the physicians is tedious since it requires reviewing the video extracted from the capsule and analysing all of its frames. This tedious task has fuelled the efforts of researchers to provide automated diagnostic techniques for WCE frameworks to detect symptoms of gastrointestinal illness. In this paper, a new computer-aided diagnosis method for abnormalities detection in WCE images is proposed. After a preprocessing step, we extract from these images the descriptor we feed to a kernel extreme learning machine to perform the classification process. The descriptor used in this work is a combination between the histogram of oriented gradients (HOG) that were extracted using the hue component of the HSV colour space, and a modified rotation-invariant local binary pattern. The proposed approach has been tested on different datasets, and the results obtained are satisfactory when compared to the state-of-the-art works.

中文翻译：

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基于极限学习机的无线胶囊内窥镜异常检测

无线胶囊内窥镜 (WCE) 是一种可以穿过人体并完全捕获小肠的设备。因此，与传统内窥镜相比，它被认为是评估胃肠道疾病的极好诊断工具。然而，医生的诊断是乏味的，因为它需要查看从胶囊中提取的视频并分析其所有帧。这项繁琐的任务推动了研究人员为 WCE 框架提供自动诊断技术以检测胃肠道疾病症状的努力。在本文中，提出了一种新的计算机辅助诊断方法，用于 WCE 图像中的异常检测。在预处理步骤之后，我们从这些图像中提取我们提供给内核极限学习机的描述符以执行分类过程。这项工作中使用的描述符是使用 HSV 颜色空间的色调分量提取的定向梯度直方图 (HOG) 和修改后的旋转不变局部二进制模式之间的组合。所提出的方法已经在不同的数据集上进行了测试，与最先进的作品相比，所获得的结果是令人满意的。