A three dimensional convolutional neural network pulmonary nodule detection algorithm based on the multi-scale attention mechanism.
10.7507/1001-5515.202011058
- Author:
Yudu ZHAO
1
;
Zhenwei PENG
1
;
Jun MA
1
;
Hao XIA
1
;
Honglin WAN
1
Author Information
1. Key Laboratory of Medical Physical Image Processing Technology, School of Physics and Electronic Science, Shandong Normal University, Jinan 250358, P. R. China.
- Publication Type:Journal Article
- Keywords:
Attention mechanism;
Multi-scale feature extraction;
Pulmonary nodule detection;
Three dimensional convolutional neural network
- MeSH:
Algorithms;
Humans;
Lung Neoplasms/diagnostic imaging*;
Neural Networks, Computer;
Radiographic Image Interpretation, Computer-Assisted/methods*;
Tomography, X-Ray Computed/methods*
- From:
Journal of Biomedical Engineering
2022;39(2):320-328
- CountryChina
- Language:Chinese
-
Abstract:
Early screening based on computed tomography (CT) pulmonary nodule detection is an important means to reduce lung cancer mortality, and in recent years three dimensional convolutional neural network (3D CNN) has achieved success and continuous development in the field of lung nodule detection. We proposed a pulmonary nodule detection algorithm by using 3D CNN based on a multi-scale attention mechanism. Aiming at the characteristics of different sizes and shapes of lung nodules, we designed a multi-scale feature extraction module to extract the corresponding features of different scales. Through the attention module, the correlation information between the features was mined from both spatial and channel perspectives to strengthen the features. The extracted features entered into a pyramid-similar fusion mechanism, so that the features would contain both deep semantic information and shallow location information, which is more conducive to target positioning and bounding box regression. On representative LUNA16 datasets, compared with other advanced methods, this method significantly improved the detection sensitivity, which can provide theoretical reference for clinical medicine.
