Efficient attention feature pyramid network for pulmonary nodule detection

VernacularTitle:高效注意力金字塔网络在肺结节检测的应用
Author: Qiong ZHANG ^{1
,

2} ; Yiliu HANG ; Jianlin QIU ; Fang WU
Author Information

1. 南通理工学院计算机与信息工程学院,江苏南通 226000
2. 南通市虚拟现实与云计算重点实验室,江苏南通 226000
Keywords: pulmonary nodule; depthwise separable convolutional neural network; attention mechanism; feature pyramid; object detection
From: Chinese Journal of Medical Physics 2024;41(11):1361-1369
CountryChina
Language:Chinese
Abstract: To address the challenge of unclear features and difficulties in pulmonary nodule CT image detection,an efficient attention feature pyramid network is proposed.The network firstly employs a feature pyramid of multi-scale feature fusion as the backbone network for effectively preserving both low-and high-level features,and uses the depthwise separable convolutional neural network to extract feature information.Then,the attention mechanism is integrated into the backbone network for assigning weights to salient feature information.Finally,the proposed algorithm is applied to Lung-PET-CT-Dx dataset and Luna16 dataset,and the experimental results demonstrate that the proposed algorithm has higher precision,recall rate and mAP value than the existing comparative algorithms,substantiating its superiority in pulmonary nodule detection.