Comparative Analysis of Brain Tumor Classification Using CT, MRI, and Fusion of CT and MRI Images with GLCM Features
Keywords:
Brain Tumor Analysis, Computed Tomography (CT) Image, Gray-Level Co-occurrence Matrix (GLCM), Local Binary Patterns (LBP), Magnetic Resonance Imaging (MRI)Abstract
Classifying brain tumors is essential for efficient diagnosis and therapy planning. The categorization of brain tumors using computed tomography (CT), magnetic resonance imaging (MRI), and fusion of CT and MRI images is compared in this study. In order to capture the unique properties of brain tumors, the study focuses on texture-based feature extraction techniques, such as Gray-Level Co-occurrence Matrix (GLCM), First-Order Statistics (FOS), and Local Binary Patterns (LBP). The classification models are trained and assessed using a dataset of fusion, CT, and MRI images of brain tumors. The tumors are classified using Support Vector Machine (SVM) on the basis of the features that were extracted. The classification results are assessed using performance metrics such area under the curve (AUC), sensitivity, specificity, and accuracy. The experimental results demonstrate that the fusion of CT and MRI images with texture-based features outperforms individual modalities in terms of classification accuracy. The study also provides insights into the importance of feature selection and classifier optimization in improving the classification performance. Overall, the proposed approach shows promising results for accurate and reliable brain tumor classification, which is essential for enhancing patient care and treatment outcomes.
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