Dual attention mechanisms with patch-level significance embedding for ischemic stroke classification in brain CT images

Stroke is currently a major contributor to disability and mortality across the globe, with ischemic stroke (IS) being the most predominant subtype. Accurate and timely diagnosis is critical for effective treatment. This study introduces a novel deep learning framework that leverages patch-level significance analysis for precise identification of ischemic strokes in computed tomography (CT) images. Our approach integrates a dual attention mechanism dynamic and cross attention with hybrid convolutional kernels to analyze the relative importance of brain regions in stroke diagnosis. The proposed architecture captures both fine-grained and contextual features to identify significant regions through attention-weighted feature embedding. The framework is evaluated on a dataset of 2023 CT of four different classes (i.e., acute: 361, chronic: 267, subacute: 382, and normal: 1013 images), employing both four and nine non-overlapping path configurations. Experimental results demonstrate that the light gradient boosted machine classifier achieved the highest patch identification accuracy of 94.81% and the extra tree classifier achieved an accuracy of 99.51% for classification using 4-patch configuration analysis. The study highlights the importance of features obtained from dense layers in mitigating overfitting and improving generalization. In addition, the study reveals the potential of attention modules with interpretable factors for patch identification of cerebral infarction, suggesting the potential of AI in aiding medical diagnosis.