A Polyp Detection Method based on FBnet

Wan, Jingjing; Chen, Tai-Yue; Chen, Bo-Lun; Yu, Yongtao; Sheng, Yi-Yun; Ma, Xiaojing

doi:10.32604/cmc.2020.010098

Cited by 4 publications

(1 citation statement)

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“…[17] had used a real-time object detector based on YOLO to detect colonic polyps. There are also some other colonic polyps detection methods based on deep learning [18][19][20][21]. The gastric polyps in our dataset are different from the colonic polyps in the pubic datasets.…”

Section: Introductionmentioning

confidence: 99%

Gastric polyp detection in gastroscopic images using deep neural network

et al. 2021

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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%.

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Section: Introductionmentioning

confidence: 99%