A multibranch fusion network (MBF-Net) for intracranial large-vessel occlusion classification.

Background: Large-vessel occlusion (LVO)-induced acute ischemic stroke (AIS) poses a significant threat to human health, with high mortality and disability rates. Rapid and accurate diagnosis of LVO via computed tomography angiography (CTA) is essential for improving patient outcomes. However, existing deep learning models often focus on binary classification and do not adequately address the challenges associated with the long-tail effect and class imbalance in real-world medical data. This study aimed to develop a multibranch fusion network (MBF-Net) that effectively classifies intracranial LVO subtypes from CTA images, mitigating the impact of data imbalance and improving diagnostic accuracy. Methods: In this paper, we propose MBF-Net for the classification of occlusion-responsible blood vessels in intracranial LVOs. The MBF-Net leverages multiple branches with distinct learning focuses to alleviate the long-tail effect. We introduce the branch hierarchical deep aggregation (BHDA) module and the semantic information enhancement (SFE) module to enhance the network's ability to extract richer spatial and semantic information. Additionally, an auxiliary attention guidance module (AAGM) is incorporated to guide the model's focus during training, resulting in visual interpretations that align with radiologists' assessments. Results: We conducted comprehensive experiments on LVO image datasets, comparing our MBF-Net with several state-of-the-art methods. Our model achieved superior performance, with a sensitivity of 94.44%, a precision of 89.29%, and an F1-score of 83.37% on the LVO classification task. These results demonstrate the effectiveness of our approach in handling class imbalance and improving diagnostic accuracy. Conclusions: The proposed MBF-Net model provides an advanced solution for the classification of LVO in medical imaging, offering high accuracy and interpretability. The model's performance on imbalanced medical imaging datasets underscores its potential for clinical application.