Abstract

Info

Title:: Efficient attention feature pyramid network for pulmonary nodule detection

Author(s):: ZHANG Qiong1; 2; HANG Yiliu1; 2; QIU Jianlin1; 2; 3; WU Fang1; 2; 1. School of Computer and Information Engineering, Nantong Institute of Technology, Nantong 226000, China 2. Nantong Key Laboratory of Virtual Reality and Cloud Computing, Nantong 226000, China 3. School of Information Science and Technology, Nantong University, Nantong 226000, China

Keywords:: Keywords: pulmonary nodule depthwise separable convolutional neural network attention mechanism feature pyramid object detection

Abstract:: 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.