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This study explored the morphological classification problem of the left atrial appendage. Given the diversity and unequal distribution of left atrial appendage categories, we propose a deep learning network based on attention mechanism. As the source of cardioembolic stroke and thromboembolism, occlusion of left atrial appendage is an important therapeutic approach. Therefore, accurate classify-cation of left atrial appendage morphology is crucial for the success of the surgery. By incorporating the attention mechanism, the model can effectively concentrate on image features, leading to improved classification performance and mitigating class imbalance concerns. Experimental results show that this method outperforms existing techniques in classifying left atrial appendage morphology, achieving an accuracy of 0.6584. It outperforms various classification networks and performs well in handling imbalanced data categories. The research findings have clinical significance for preoperative planning and postoperative recovery of left atrial appendage occlusion surgery.
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