Abstract

Stroke remains one of the leading causes of disability and mortality worldwide, necessitating timely and accurate diagnosis to improve treatment outcomes. This study presents a computer-aided diagnosis (CAD) system designed to detect and classify stroke lesions in magnetic resonance imaging (MRI), specifically utilizing diffusion-weighted imaging (DWI) sequences. A hybrid segmentation technique, fuzzy c-means with active contour (FCMAC), is proposed to enhance lesion localization accuracy. For classification, the system evaluates traditional machine learning algorithms like support vector machine (SVM) and k-nearest neighbor (KNN), alongside deep learning models such as convolutional neural network (CNN) and bilayered neural network (BNN). The entire diagnostic pipeline is integrated into a MATLAB-based graphical user interface (GUI), facilitating real-time analysis and ease of use in clinical settings. Experimental results show that the proposed FCMAC method achieves a dice coefficient (DC) of 0.654, outperforming conventional segmentation techniques. Among the classifiers, KNN offered the best balance between prediction accuracy and computational efficiency. The final system, termed SmartStroke-Pro, enables early detection and classification of stroke, providing a reliable and practical tool to assist healthcare professionals, particularly in resource-limited environments. This framework has the potential to reduce diagnostic delays and support improved clinical decision-making in acute stroke care.

Keywords

Stroke diagnosis, Computer-aided diagnosis (CAD), DWI, Machine learning, Fuzzy c-means with active contour (FCMAC), SmartStroke-pro system.

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