CNN-TriFuseResNet-50: a hybrid deep learning model for efficient and interpretable detection of middle cerebral artery occlusion

Acute ischemic stroke (AIS) is a time-critical medical emergency where high-performance computing (HPC) capabilities are essential to enable real-time diagnosis and rapid treatment decision-making. Acute ischemic stroke caused by middle cerebral artery occlusion (MCAO) without hyperdense artery sign (HAS) on non-contrast CT (NCCT) poses significant diagnostic challenges due to subtle imaging features. To address this, we propose CNN-TriFuseResNet-50, a novel deep learning architecture integrating Triplet Attention, pre-trained ResNet-50, and lightweight convolutional blocks for automated HAS-negative MCAO detection. The Triplet Attention mechanism concurrently models spatial (width, height) and channel-wise dependencies, resolving feature conflicts inherent in conventional attention modules like Squeeze-and-Excitation (SE) and Convolutional Block Attention Module (CBAM). Evaluated on a retrospective cohort of 822 cases, our architecture achieves 81.66% accuracy and 0.90 AUC, outperforming Vision Transformers (ViT) by 4.37% accuracy with 58% fewer parameters. This architecture reduces false negatives by 24% (84.52% sensitivity) and achieves real-time inference (23 frames per second [FPS]), satisfying the stringent latency requirements of high-performance medical imaging systems. Class activation maps (CAMs) provide interpretability by localizing occlusion regions aligned with angiographic ground truth. This work bridges the gap between computational efficiency and diagnostic accuracy, offering a deployable solution for early stroke management in resource-constrained settings.