Authors : Dr. Mitat Uysal; Dr. Aynur Uysal

Volume/Issue : Volume 10 - 2025, Issue 4 - April

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Google Scholar : https://tinyurl.com/fvwe6wat

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DOI : https://doi.org/10.38124/ijisrt/25apr1680

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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.

Keywords : Brain Tumor, Machine Learning, Logistic Regression, Mathematical Modeling, Diffusion-Reaction Equation, Tumor Growth, Artificial Intelligence.

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