Combination of CNN and Transformer for Lesion-Guided Honeycomb Lung CT Image Recognition
Honeycomb lung is a CT imaging manifestation of various advanced lung diseases,characterized by diverse cystic lesions presenting a honeycomb-like appearance.Existing computer-aided diagnosis methods struggle to effectively address the low identification accuracy caused by the varied morphology and different locations of cellular lung lesions.Therefore,a combined CNN and Transformer model guided by lesion signals is proposed for cellular lung CT image recognition.In this model,a multi-scale information enhancement module is first employed to enrich the spatial and channel information of features obtained by CNN at different scales.Simultaneously,a lesion signal generation module is used to strengthen the expression of lesion features.Subsequently,Transformer is utilized to capture long-range dependency information of features,compensating for the deficiency of CNN in extracting global information.Finally,a multi-head cross-attention mechanism is introduced to fuse feature information and obtain classification results.Experimental results demonstrate that the proposed model achieves accuracies of 99.67%and 97.08%on the honeycomb lung and COVID-CT dataset,respectively.It outperforms other models,providing more precise recognition results and validating the effectiveness and generalization of the model.
image processinghoneycomb lunglesion signalmulti-scale information enhancementcross attention
NETL
NSTL
万方数据
