Pulmonary nodule classification method based on shifted window attention and codec
Existing methods for pulmonary nodule classification still exist problems such as lack of interpretability in reasoning process and discriminative feature representation.To address these issues,pulmonary nodule classification network based on shifted window attention and codec(SWAC)was proposed.The SWAC model combines the advantages of convolutional neural networks(CNN)and the shifted window attention mechanism,effectively extracted shallow and deep features of nodules by focusing on the necessary regions for classification.The CNN introduces the Focal loss function to constrain the main network's features and focus on difficult samples,thus improving the discriminative representation ability of the network.The contribution and impact of each part of the method was analyzed through ablation experiments on the LIDC-IDRI dataset.The results showed that the proposed method has excellent performance in pulmonary nodule classification.
classification of pulmonary nodulesdeep learningattention mechanism
万方数据
维普
