An efficient deep learning model that can be implemented in real-time for
polyp detection is crucial to reducing polyp miss-rate during screening
procedures. Convolutional neural networks (CNNs) are vulnerable to small
changes in the input image. A CNN-based model may miss the same polyp appearing
in a series of consecutive frames and produce unsubtle detection output due to
changes in camera pose, lighting condition, light reflection, etc. In this
study, we attempt to tackle this problem by integrating temporal information
among neighboring frames. We propose an efficient feature concatenation method
for a CNN-based encoder-decoder model without adding complexity to the model.
The proposed method incorporates extracted feature maps of previous frames to
detect polyps in the current frame. The experimental results demonstrate that
the proposed method of feature concatenation improves the overall performance
of automatic polyp detection in videos. The following results are obtained on a
public video dataset: sensitivity 90.94\%, precision 90.53\%, and specificity
92.46%

该研究提出一种集成时间信息的有效特征级联方法，用于改善基于卷积神经网络的结构在视频直肠镜筛查中对息肉的自动检测，实验结果表明，该方法可提高自动检测的整体性能。