Beijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China.
Institute of Automation, Chinese Academy of Sciences, Beijing, China.
PLoS One. 2021 Apr 28;16(4):e0250632. doi: 10.1371/journal.pone.0250632. eCollection 2021.
This paper presents the research results of detecting gastric polyps with deep learning object detection method in gastroscopic images. Gastric polyps have various sizes. The difficulty of polyp detection is that small polyps are difficult to detect from the background. We propose a feature extraction and fusion module and combine it with the YOLOv3 network to form our network. This method performs better than other methods in the detection of small polyps because it can fuse the semantic information of high-level feature maps with low-level feature maps to help small polyps detection. In this work, we use a dataset of gastric polyps created by ourselves, containing 1433 training images and 508 validation images. We train and validate our network on our dataset. In comparison with other methods of polyps detection, our method has a significant improvement in precision, recall rate, F1, and F2 score. The precision, recall rate, F1 score, and F2 score of our method can achieve 91.6%, 86.2%, 88.8%, and 87.2%.
本文提出了一种基于深度学习目标检测方法在胃镜图像中检测胃息肉的研究结果。胃息肉大小不一。息肉检测的难点在于从小背景中检测出小息肉。我们提出了一种特征提取和融合模块，并将其与 YOLOv3 网络相结合，形成我们的网络。该方法在小息肉的检测中表现优于其他方法，因为它可以融合高层特征图的语义信息与低层特征图，以帮助小息肉的检测。在这项工作中，我们使用了自己创建的胃息肉数据集，其中包含 1433 张训练图像和 508 张验证图像。我们在自己的数据集上训练和验证我们的网络。与其他息肉检测方法相比，我们的方法在精度、召回率、F1 和 F2 分数方面有显著提高。我们的方法的精度、召回率、F1 分数和 F2 分数分别可以达到 91.6%、86.2%、88.8%和 87.2%。
