DESIGN AND IMPLEMENTING BRAIN TUMOR DETECTION USING MACHINE LEARNING APPROACH

This study focuses on the development of a robust Brain Tumor Detection system employing a Machine Learning (ML) approach, primarily utilizing Magnetic Resonance Imaging (MRI) data. The methodology involves a comprehensive pipeline encompassing pre-processing, segmentation, and feature extraction techniques to enhance the quality of input data for subsequent analysis. Various machine learning algorithms, with a particular emphasis on the Support Vector Machine (SVM), are employed for classification tasks to distinguish between normal and abnormal brain tissues. Performance metrics such as Root Mean Square Error (RMSE), recall, sensitivity, precision, F-score, specificity, Probability of Misclassification Error (PME), and accuracy are utilized to assess the model's efficacy. The integration of these metrics allows for a thorough evaluation of the detection system, providing insights into its precision, sensitivity to abnormalities, and overall accuracy in classifying brain tumor cases. The proposed approach contributes to the advancement of medical diagnostics through the fusion of advanced imaging technologies and machine learning methodologies for improved brain tumor detection and classification.

Keywords: Magnetic Resonance Imaging (MRI); Brain tumor; Pre-processing; Segmentation; feature extraction; machine learning techniques.