Abstract
Accurate and early detection of intracranial aneurysms is critical for preventing life-threatening subarachnoid hemorrhage and improving clinical outcomes. This study proposes a hybrid diagnostic framework that integrates radiomics-based feature engineering with a transformer-driven deep learning architecture enhanced by teacher–student contrastive representation learning. The workflow incorporates region-of-interest segmentation, handcrafted radiomic feature extraction, multimodal representation fusion, and probabilistic aneurysm localization using high-resolution MR and MRA imaging. Comprehensive experiments conducted on benchmark neuroimaging datasets demonstrate that the proposed model achieves high classification accuracy, stable convergence, and robust generalization across diverse anatomical and imaging conditions. Qualitative evaluations further reveal that heatmap-based confidence overlays reliably identify aneurysmal regions and closely align with ground-truth annotations. The contrastive learning module strengthens spatial and frequency-domain feature alignment, enabling effective training under limited supervision and reducing performance degradation associated with data heterogeneity. While limitations remain regarding dataset breadth and segmentation dependencies, the results indicate that this hybrid radiomics–AI framework offers a promising pathway toward automated aneurysm screening and clinical decision support. The proposed system has the potential to enhance diagnostic precision, mitigate inter-observer variability, and contribute to earlier intervention in neurovascular care.