Brain Tumor Classification: A Comprehensive Framework with Uncertainty Quantification and Markov Chain Simulation

Brain tumor is an abnormal growth of cells within the brain or its surrounding tissues that can disrupt normal brain function. Brain tumors can be classified as benign (non-cancerous) or malignant (cancerous), with each requiring different diagnostic and therapeutic approaches. The proposed methodology aims to develop a machine learning-based brain tumor classification system using medical imaging data, such as Magnetic Resonance Imaging (MRI) scans. The objective is to automatically identify and categorize tumor types, including glioma, meningioma, and pituitary tumors, while distinguishing benign from malignant cases. It would comprise pre-processing MRI images for removing noise and improving clarity, feature extraction, and employing advanced machine learning techniques, like Convolutional Neural Networks or transfer learning frameworks, to perform classification. The model was tested against an expansive dataset of annotated brain tumor images to achieve high accuracy and robustness. Evaluation metrics include accuracy which is 99.31% for 2D images and 90.68% for 3D images, precision, recall, and F1-score to judge the performance of the model. The presented system can help health professionals make rapid and reliable diagnoses, resulting in better care and treatment.