Stroke is one of the leading causes of disability and mortality worldwide, with ischemic and haemorrhagic strokes being the two primary types. Early and accurate detection of these stroke types from medical imaging, such as CT scans, is crucial for timely intervention. This study proposes a deep learning-based approach for automated classification of ischemic stroke, haemorrhagic stroke, and normal brain scans using a modified ResNet50V2 architecture enhanced with a Channel Attention Mechanism (CAM). The dataset, comprising CT scan images, was pre-processed and balanced using Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance. Data augmentation techniques were employed to improve model generalization. The proposed model utilizes ResNet50V2 as the feature extractor while integrating CAM to refine feature representation by emphasizing important channels. The final classification layer outputs three categories using a SoftMax activation function. The model was trained using categorical cross-entropy loss and optimized with the Adam optimizer. Experimental results demonstrated an overall accuracy of 99%, with class- wise F1-scores exceeding 97%, indicating robust performance in stroke classification. Additionally, Grad-CAM visualization was employed to enhance interpretability by highlighting critical regions in the input images influencing model decisions. The proposed approach provides an efficient and explainable deep learning solution for automated stroke detection, potentially aiding radiologists in early diagnosis and reducing clinical workload.
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