Brain stroke detection and classification using deep learning-based transCBAMSegNet and hybrid transformer models

Abstract

Stroke is a life-threatening neurological condition that requires rapid assessment to reduce mortality and long-term disability. Accurate lesion segmentation and reliable stroke type classification from brain computed tomography (CT) images are essential to support timely clinical decision-making. In this study, a deep learning-based framework is proposed for automatic stroke lesion segmentation and CT-based stroke type classification. In the segmentation stage, the developed TransCBAMSegNet model achieved an accuracy of 99.60%, a mean intersection over union (mIoU) of 75.98%, and a Dice Similarity Score (DSS) of 75.50%. In the classification stage, a hybrid MaxxViT-ViT-SwinV2 feature fusion strategy achieved an accuracy of 97%, enabling reliable differentiation between acute/hyperacute ischaemic stroke, haemorrhagic stroke, and normal cases. Experimental results show that the proposed multi-transformer fusion approach provides improved performance compared to single-backbone configurations under identical evaluation settings. The framework is designed as a computer-aided CT-based stroke assessment tool and may support early-stage imaging-based decision processes. Overall, the proposed methodology offers a robust and reproducible approach for automated stroke analysis using CT imaging data.