BRAIN TUMOUR DETECTION AND SEGMENTATION USING MACHINE LEARNING

Automatic segmentation of MR images of normal brains by statistical classification, using an atlas before initialization and also for geometric constraints. A most recent extension detects brain lesions as outliers and is successfully applied for the detection of multiple sclerosis lesions. Brain tumors, however, can’t be modeled as intensity outliers due to overlapping intensities with normal tissue and significant size. Brain tumors are one of the most serious and life-threatening medical conditions that can affect individuals of all ages. Early detection and diagnosis are crucial for the management or treatment of brain tumors. Machine learning techniques, such as Support Vector Machines (SVM), have been applied in the medical field to aid in detecting brain tumors. In this approach, SVM is used along with data structures to analyze patient data and make predictions about the presence of brain tumors. Overall, the SVM and data structure approach can provide an effective tool for the detection and segmentation of brain tumors, potentially improving patient outcomes and quality of life.