This paper presents an intelligent system for gastrointestinal polyp detection in endoscopic video. Video endoscopy is a popular diagnostic modality in assessing the gastrointestinal polyps. But the accuracy of diagnosis mostly depends on doctors' experience that is crucial to detect polyps in many cases. Computer‐aided polyp detection is promising to reduce the miss detection rate of polyp and thus improve the accuracy of diagnosis results. The proposed method illustrates an automatic system based on a new color feature extraction scheme as a support for gastrointestinal polyp detection. The scheme is the combination of color empirical mode decomposition features and convolutional neural network features extracted from video frames. The features are fed into a linear support vector machine to train the classifier. Experiments on standard public databases show that the proposed scheme outperforms the previous conventional methods, gaining accuracy of 99.53%, sensitivity of 99.91%, and specificity of 99.15%.

中文翻译：

---

一种融合二维经验模式分解和卷积神经网络特征的内窥镜视频胃肠息肉检测智能系统

本文提出了一种内窥镜视频中胃肠道息肉检测的智能系统。视频内窥镜检查是评估胃肠道息肉的流行诊断方式。但诊断的准确性主要取决于医生的经验，这在许多情况下对检测息肉至关重要。计算机辅助息肉检测有望降低息肉的漏检率，从而提高诊断结果的准确性。所提出的方法说明了基于新颜色特征提取方案的自动系统，作为对胃肠息肉检测的支持。该方案是结合颜色经验模式分解特征和从视频帧中提取的卷积神经网络特征。特征被送入线性支持向量机以训练分类器。