Despite today's developing healthcare technology, conventional colonoscopy is still a gold-standard method to detect colon abnormalities. Due to the folded structure of the intestine and visual disturbances caused by artifacts, it can be hard for specialists to detect abnormalities during the procedure. Frames that include artifacts such as specular reflection, improper contrast levels from insufficient or excessive illumination gastric juice, bubbles, or residuals should be detected to increase an accurate diagnosis rate. In this work, both conventional machine learning and transfer learning methods have been used to detect non-informative frames in colonoscopy videos. The conventional machine learning part consists of 5 different types of texture features, which are gray level co-occurrence matrix (GLCM), gray level run length matrix (GLRLM), neighborhood gray-tone difference matrix (NGTDM), focus measure operators (FMOs), and first-order statistics. In addition to these methods, we utilized 8 different transfer learning models: AlexNet, SqueezeNet, GoogleNet, ShuffleNet, ResNet50, ResNet18, NasNetMobile, and MobileNet. The results showed that FMOs and decision tree combination gave the best accuracy and f-measure values with almost 89% and 0.79%, respectively, for the conventional machine learning part. When the transfer learning part is taken into account, AlexNet (99.85%) and SqueezeNet (98.80%) have the highest performance metric results. This study shows the potential of both transfer learning and conventional machine learning algorithms to provide fast and accurate non-informative frame detection to be used during a colonoscopy, which may be considered the initial step in identifying and classifying colon-related diseases automatically to help guide physicians.

中文翻译：

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结肠镜检查视频中自动非信息帧检测的综合研究

尽管当今的医疗技术不断发展，传统结肠镜检查仍然是检测结肠异常的金标准方法。由于肠道的折叠结构和伪影引起的视觉障碍，专家在手术过程中很难发现异常情况。应检测包括镜面反射、由于照明不足或过度照明而导致的对比度不正确、胃液、气泡或残留物等伪像的帧，以提高准确的诊断率。在这项工作中，传统的机器学习和迁移学习方法都被用来检测结肠镜检查视频中的非信息帧。常规机器学习部分由5种不同类型的纹理特征组成，分别是灰度共生矩阵（GLCM）、灰度游程矩阵（GLRLM）、邻域灰度差值矩阵（NGTDM）、焦点测量算子（FMOs） ）和一阶统计量。除了这些方法之外，我们还利用了 8 种不同的迁移学习模型：AlexNet、SqueezeNet、GoogleNet、ShuffleNet、ResNet50、ResNet18、NasNetMobile 和 MobileNet。结果表明，对于传统机器学习部分，FMO 和决策树组合给出了最佳准确率和 f 测量值，分别接近 89% 和 0.79%。当考虑到迁移学习部分时，AlexNet (99.85%) 和 SqueezeNet (98.80%) 具有最高的性能指标结果。这项研究显示了迁移学习和传统机器学习算法在结肠镜检查期间提供快速、准确的非信息性帧检测的潜力，这可能被认为是自动识别和分类结肠相关疾病以帮助指导的第一步医生。