Machine Learning-Enhanced Models in Brain Tumors: A Mathematical and Computational Perspective
Year: 2025
Type: article
Abstract: 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.
Authors: Mitat Uysal, Aynur Uysal
Institution: Doğuş University
Language: English
Subfield: Modeling and Simulation
Field: Mathematics
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4410162281