Lung nodule classification algorithm based on multi-dimensional fusion
10.3969/j.issn.1005-202X.2024.11.016
- VernacularTitle:基于多维度融合的肺结节分类算法
- Author:
Hongqun DU
1
;
Yueyang LI
;
Fangzheng CUI
;
Haichi LUO
;
Zhongxuan GU
Author Information
1. 江南大学附属医院影像科,江苏无锡 214122
- Keywords:
lung nodule;
computer-aided diagnosis;
multi-scale feature fusion;
soft activation mapping;
balanced mean square error loss
- From:
Chinese Journal of Medical Physics
2024;41(11):1428-1436
- CountryChina
- Language:Chinese
-
Abstract:
A novel algorithm based on multi-dimensional fusion is proposed for classifying lung nodules.Based on the algorithm for reducing false positives of pulmonary nodules,the optimization is carried out by introducing a high-level feature enhancement soft activation mapping module after obtaining features by the multi-scale feature fusion module to improve the classification ability.To address the imbalance of different nodule data in the actual classification,a balanced mean square error loss is adopted to improve the training effect of the model.A three-dimensional and two-dimensional model fusion method is used to further improve the classification performance.The experiment conducted on a Private Lung dataset proves that the proposed model has a classification accuracy of 93.8％,outperforming the existing methods.
