Gastric polyp detection in gastroscopic images using deep neural network

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%.

Automated summary

This paper introduces a method for detecting gastric polyps in gastroscopic images using feature extraction and fusion module combined with the YOLOv3 network. The network was trained and validated on a self-created dataset, showing significant improvement in precision, recall rate, F1, and F2 score, particularly in the detection of small polyps.