Intracranial hemorrhage (ICH) is a critical medical emergency caused by the rupture of cerebral blood vessels, leading to internal bleeding within the skull. Accurate and timely classification of hemorrhage subtypes is essential for effective clinical decision-making. To address this challenge, we propose an advanced pyramid vision transformer (PVT)-based model, leveraging its hierarchical attention mechanisms to capture both local and global spatial dependencies in brain CT scans. Instead of processing all extracted features indiscriminately, A SHAP-based feature selection method is employed to identify the most discriminative components, which are then used as a latent feature space to train a boosting neural network, reducing computational complexity. We introduce an entropy-aware aggregation strategy along with a fuzzy integral operator to fuse information across multiple CT slices, ensuring a more comprehensive and reliable scan-level diagnosis by accounting for inter-slice dependencies. Experimental results show that our PVT-based framework significantly outperforms state-of-the-art deep learning architectures in terms of classification accuracy, precision, and robustness. By combining SHAP-driven feature selection, transformer-based modeling, and an entropy-aware fuzzy integral operator for decision fusion, our method offers a scalable and computationally efficient AI-driven solution for automated ICH subtype classification.

翻译：颅内出血（ICH）是一种由脑血管破裂引起的危急医疗紧急情况，导致颅腔内出血。准确及时地对出血亚型进行分类对于有效的临床决策至关重要。为应对这一挑战，我们提出了一种基于先进金字塔视觉Transformer（PVT）的模型，利用其分层注意力机制来捕获脑部CT扫描中的局部和全局空间依赖性。我们并未不加区分地处理所有提取的特征，而是采用一种基于SHAP的特征选择方法来识别最具判别性的成分，随后将这些成分用作潜在特征空间来训练一个提升神经网络，从而降低计算复杂度。我们引入了一种熵感知聚合策略以及模糊积分算子，以融合多个CT切片间的信息，通过考虑切片间的依赖性，确保更全面可靠的扫描级诊断。实验结果表明，我们基于PVT的框架在分类准确性、精确性和鲁棒性方面显著优于当前最先进的深度学习架构。通过结合SHAP驱动的特征选择、基于Transformer的建模以及用于决策融合的熵感知模糊积分算子，我们的方法为自动化ICH亚型分类提供了一个可扩展且计算高效的人工智能驱动解决方案。