Vision Transformer for Intracranial Hemorrhage Classification in CT Scans Using an Entropy-Aware Fuzzy Integral Strategy for Adaptive Scan-Level Decision Fusion
Journal:
arXiv
Published Date:
Mar 11, 2025
Abstract
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.
