A standard screening procedure involves video endoscopy of the Gastrointestinal tract. It is a less invasive method which is practiced for early diagnosis of gastric diseases. Manual inspection of a large number of gastric frames is an exhaustive, time-consuming task, and requires expertise. Conversely, several computer-aided diagnosis systems have been proposed by researchers to cope with the dilemma of manual inspection of the massive volume of frames. This article gives an overview of different available alternatives for automated inspection, detection, and classification of various GI abnormalities. Also, this work elaborates techniques associated with content-based image retrieval and automated systems for summarizing endoscopic procedures. In this survey, we perform a comprehensive review of feature extraction techniques and deep learning methods which were specifically developed for automatic analysis of endoscopic videos. In addition, we categorize features extraction techniques according to image processing domains and further we classify them based on their visual descriptions. We also review hybrid feature extraction techniques which are developed by the fusion of different kind of basic descriptors. Moreover, this survey covers various endoscopy data-sets available for the bench-marking of vision based algorithms. On the basis of literature, we explain emerging trends in computerized analysis of endoscopy. We also survey important issues, challenges, and future research directions to the development of computer-assisted systems for detection of maladies and interactive surgery in the GI tract.

中文翻译：

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基于深度学习的胃肠道视频内窥镜异常检测特征提取与融合研究

标准的筛查程序包括胃肠道的视频内窥镜检查。这是一种侵入性较小的方法，用于早期诊断胃病。手动检查大量胃框架是一项详尽、耗时的任务，需要专业知识。相反，研究人员已经提出了几种计算机辅助诊断系统来应对手动检查大量帧的困境。本文概述了各种 GI 异常的自动检查、检测和分类的不同可用替代方案。此外，这项工作详细阐述了与基于内容的图像检索和用于总结内窥镜程序的自动化系统相关的技术。在本次调查中，我们对专为自动分析内窥镜视频而开发的特征提取技术和深度学习方法进行了全面审查。此外，我们根据图像处理领域对特征提取技术进行分类，并进一步根据其视觉描述对它们进行分类。我们还回顾了通过融合不同类型的基本描述符而开发的混合特征提取技术。此外，该调查涵盖了可用于对基于视觉的算法进行基准测试的各种内窥镜数据集。在文献的基础上，我们解释了内窥镜计算机化分析的新兴趋势。我们还调查了重要的问题、挑战、