Multi-Feature Extraction of Pulmonary Nodules Based on LSTM and Attention Structure

The accurate classification of shape, edge, and internal features of pulmonary nodules can not only assist the radiologists in their daily diagnosis, but also improve the writing efficiency of imaging reports. This paper proposes a multi-task classification model based on long-short term memory (LSTM) and attention structure, which merges the shared features among different classification tasks through attention mechanism to improve the feature extraction performance of the current task. The classifier based on temporal sequence LSTM structure can effectively screen the shared features and improve the efficiency of information transmission in the multi-task model. Experimental results show that compared with the traditional multi-task structure, the proposed model can achieve better classification results on the public dataset LIDC-IDRI, and assist doctors to quickly obtain nodule characteristics.