关键词: 肺部病灶, 长时程, 特征融合, 长短时记忆模型

Abstract: In order to characterize the changes and development rules of the lesion characteristics in various stages of pulmonary 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.

Key words: pulmonary lesions, long term, feature fusion, long and short time memory model