<![CDATA[To detect brain bleeding in CT images, this study presents an improved Local Binary Pattern (NLBP) operator for texture analysis in medical imaging. The suggested NLBP utilizes an XOR operation with multi-radius feature extraction (r=1 and r=2) to capture fine-grained and larger texture patterns. Applied techniques compare pixel intensities over two radii and use the NLBP operator on image patches. To emphasize sudden changes in texture, the binary patterns produced by these two radii were processed using XOR to highlight variations in pixel intensities. To achieve the goal of this study, four machine learning models were applied to the CT brain images dataset to identify hemorrhage cases from non-hemorrhagic. According to the results, the NLBP approach considerably improved classification performance over conventional LBP. The random forest algorithm achieved a superior prediction accuracy of 94.05% while employing the NLBP strategy for feature extraction, in contrast to only 70.03% accuracy obtained using the LBP method for a similar algorithm. The NLBP approach improved edge recognition and classification accuracy by highlighting differences between surrounding pixel brightness and capturing multi-scale texture information. It concluded from these results that the NLBP operator provides a reliable method for medical image analysis by combining XOR-based refinement with multi-radius extraction. Additional investigation may examine the use of NLBP in different imaging modalities and refine the feature selection procedure for enhanced performance in various settings.]]>
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