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View all- Lee JHsu YChien J(2024)Study of a Deep Convolution Network with Enhanced Region Proposal Network in the Detection of Cancerous Lung TumorsBioengineering10.3390/bioengineering1105051111:5(511)Online publication date: 19-May-2024
Pulmonary Nodule Detection Based on RPN with Squeeze‐and‐Excitation Block
Authors: 
Xiaoxi Lu, Xingyue Wang, Jiansheng Fang, Na Zeng, Yao Xiang, Jingfeng Zhang, Jianjun Zheng, Jiang LiuAuthors Info & Claims
Pages 85 - 92
Abstract
Early detection of lung cancer is a crucial step to improve the chances of survival. To detect the pulmonary nodules, various methods are proposed including one-stage object detection methods (e.g., YOLO, SSD) and two-stage detection methods(e.g., Faster RCNN). Two-stage methods are more accurate than one-stage, thus more likely used in the detection of a small object. Faster RCNN as a two-stage method, ensuring more efficient and accurate region proposal generation, is consistent with our task’s objective, that is, detecting small 3-D nodules from large CT image volume. Therefore, in our work, we used 3-D region proposal network (RPN) proposed in Faster RCNN to detect nodules. However, different from natural images with clear boundaries and textures, pulmonary nodules have different types and locations, which are hard to recognize. Thus with the thought that if the network can learn more features of the nodules, the performance would be better, we also applied the "Squeeze-and-Excitation" blocks to the 3-D RPN, which we term it as SE-Res RPN. The experimental results show that the sensitivity of SE-Res RPN in 10-fold cross-validation of LUNA 16 is 93.7, which achieves great performance without a false positive reduction stage.
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August 2022
241 pages
ISBN:9781450397315
DOI:10.1145/3561613
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ICCCV 2022: 2022 The 5th International Conference on Control and Computer Vision
August 19 - 21, 2022
Xiamen, China
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View all- Lee JHsu YChien J(2024)Study of a Deep Convolution Network with Enhanced Region Proposal Network in the Detection of Cancerous Lung TumorsBioengineering10.3390/bioengineering1105051111:5(511)Online publication date: 19-May-2024
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