<![CDATA[Background: Stroke is one of the leading causes of death and disability worldwide. Early detection and rapid intervention are crucial to reducing the adverse effects of stroke. In the last decade, the use of computed tomography (CT) scans has become the standard in stroke diagnosis. However, the main challenge medical practitioners face is the rapid and accurate interpretation of CT scan images for early signs of stroke. Objective: The main aim is to improve the accuracy and efficiency of stroke diagnosis early, thus enabling faster and more effective medical intervention. Methods: The research methodology involves using advanced algorithms and image analysis techniques to identify early signs of stroke on CT scan images. Results: This study reviewed a series of cases of patients with early stroke symptoms, comparing the results of manual analysis by medical practitioners with those of analysis using an improved computerized approach. This study significantly improved early stroke detection using optimized CT Scan image analysis methods. Compared to traditional methods, this approach offers higher accuracy, potentially reducing the time required for diagnosis. Conclusion: This study confirms that integrating advanced image analysis technology in medical practice can be essential in early stroke diagnosis. The implications of these findings are significant, especially in improving emergency medical response and stroke management, as well as in lowering the risk of long-term damage to patients.]]>
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