计算机工程与应用2019，Vol.55Issue(10)：146-153,8.DOI:10.3778/j.issn.1002-8331.1802-0157
多模态融合下长时程肺部病灶良恶性预测方法
Benign and Malignant Prediction of Pulmonary Lesions in Long Term Based on Multimodal Fusion
张娅楠 1赵涓涓 1赵鑫 1张小龙 2王三虎3
作者信息
- 1. 太原理工大学 计算机科学与技术学院,山西 晋中 030600
- 2. 宾夕法尼亚州立大学 信息科学与技术学院,尤尼弗西蒂帕克 16802
- 3. 吕梁学院 计算机科学与技术系,山西 吕梁 033000
- 折叠
摘要
Abstract
In order to characterize the changes and development rules of the lesion characteristics in various stages of pul-monary medical images more accurately and comprehensively, and study the evolution of lung nodules in the longitudinal dimension of time, this paper constructs a prediction model of benign and malignant lung lesions at different stages based on multimodal feature fusion. Firstly, according to the CT images of different stages of the patients, extract the traditional features and depth features of lung lesions, and construct the multimodal features. Then multimodal features are fused by two layers of neural networks. Finally, long and short time memory is used to study the feature vectors of lung lesions with different time characteristics. A bidirectional long and short term memory network is constructed to predict the benign and malignant lesions. The experiments show that the accuracy rate of the proposed method is 92.8% , which is higher than that of the traditional methods, and the proposed method can achieve effective prediction.关键词
肺部病灶/长时程/特征融合/长短时记忆模型Key words
pulmonary lesions/long term/feature fusion/long and short time memory model分类
信息技术与安全科学引用本文复制引用
张娅楠,赵涓涓,赵鑫,张小龙,王三虎..多模态融合下长时程肺部病灶良恶性预测方法[J].计算机工程与应用,2019,55(10):146-153,8.
