Machine Learning-Enhanced Models in Brain Tumors: A Mathematical and Computational Perspective

Brain tumors pose a significant challenge in medical diagnostics and treatment due to their heterogeneous nature and complex growth patterns. Recent advances in machine learning (ML) have enhanced traditional modeling approaches by incorporating data-driven predictions and adaptive learning. This article explores machine learning-enhanced models for brain tumors, focusing on mathematical equations that describe tumor growth and ML techniques used for prediction and classification. We present detailed mathematical models, including diffusion-reaction equations and tumor segmentation approaches, and conclude with a Python-based example of logistic regression-based classification using only NumPy.