Abstract: This paper addresses the challenging task of brain tumor segmentation in 2D Magnetic Resonance Brain Images (MRI), recognizing the limitations of manual classification and the complexities arising from diverse tumor appearances. The comprehensive analysis employing traditional classifiers like Support Vector Machine, Multilayer Perceptron and a Convolutional Neural Network (CNN). The primary objective centers on distinguishing normal and abnormal pixels based on texture and statistical features. Notably, the CNN outperforms traditional classifiers, providing a robust foundation for accurate brain tumor segmentation. This research contributes significantly to advancing the field of medical image processing, offering a robust and efficient approach for brain tumor segmentation with room for further optimization.
Keywords: Brain tumor segmentation, Magnetic Resonance Imaging (MRI), Convolutional Neural Network (CNN), Traditional classifiers, Support Vector Machine (SVM), Multilayer perception.
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DOI:
10.17148/IJARCCE.2024.134148
