What is it about?

In this paper, to address the problems of small size of lung nodules, large proportion of background region, and complex lung structures in lung nodule detection of CT images, a weighted bidirectional recursive pyramid algorithm is presented. First, the weighted bidirectional recursive feature pyramid network (BiPRN) is proposed, which can increase the ability of network model to extract feature information, and achieve multi-scale fusion information. Second, a CBAM_CSPDarknet53 structure is developed to incorporate an attention mechanism as a feature extraction module, which can aggregate both spatial information and channel information of the feature map. The weighted BiRPN and CBAM_CSPDarknet53 are applied to the YOLOvX model for lung nodule detection experiments, named BiRPN-YOLOvX, where YOLOvX represents different versions of YOLO. The proposed method has a great potential for clinical applications to improve the sensitivity of lung nodule detection.

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Why is it important?

Improved capability for multi-scale fusion in convolutional neural networks, as well as improved fusion of channel and spatial information in the network. The results show that our algorithm improves the sensitivity of lung nodule detection.


With the increasing number of lung cancer patients in recent years, it is important that we improve the detection of lung nodules to help improve survival rates.

Liying Han
Hebei University of Technology

Read the Original

This page is a summary of: BiRPN-YOLOvX: A weighted bidirectional recursive feature pyramid algorithm for lung nodule detection, Journal of X-Ray Science and Technology, March 2023, IOS Press, DOI: 10.3233/xst-221310.
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