<![CDATA[Stroke is a major neurological disorder requiring rapid and accurate diagnosis for effective treatment. Computerized Tomography (CT) scanning provides detailed brain imaging but requires expert interpretation. This study aims to develop an automated classification system to distinguish between normal and stroke-affected brain CT scan images using texture feature analysis, providing enhanced accuracy and robustness compared to existing single-feature approaches. A total of 200 CT scan images (100 normal, 100 stroke cases) from the Kaggle database were analyzed. Texture features were extracted using Histogram, Gray Level Co-occurrence Matrix (GLCM), and Gray Level Run Length Matrix (GLRLM) analysis. The KNN algorithm was evaluated using percentage split validation, with the training set ranging from 50% to 70% of the data. The KNN classifier achieved optimal performance with 93% accuracy, 91% precision, and 96% recall using a 50% training set, demonstrating its potential as a diagnostic support tool for healthcare professionals to facilitate faster diagnosis and treatment decisions. The integration of multiple texture analysis methods showed superior performance compared to individual feature extraction techniques. Histogram features contributed significantly to classification accuracy by enhancing the detection of tissue heterogeneity. Texture analysis revealed significant differences between normal and stroke images in entropy, contrast, and correlation parameters. The proposed method successfully classifies CT scan images of normal and stroke-affected brains with high accuracy, demonstrating potential for clinical implementation in automated stroke screening and diagnostic support.]]>
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