Year: 2025
Type: article
Abstract: — Brain tumor detection systems using Explainable Artificial Intelligence (XAI) aim to provide accurate and interpretable tumor diagnosis. This research integrates machine learning models such as Decision Trees, Random Forest, Logistic Regression, and Support Vector Machines, leveraging ensemble learning techniques like stacking and voting to enhance predictive accuracy. The system employs XAI techniques such as SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-agnostic Explanations) to ensure model transparency and interpretability. This paper presents the methodology, implementation, evaluation metrics, and the impact of integrating explainability into brain tumor detection systems, emphasizing how XAI aids clinicians in understanding diagnostic decisions and improving trust in AI-driven outcomes.
DOI: 10.55041/ijsrem46252
Author: Qureshi Tamir
Language: English
Subfield: Neurology
Field: Neuroscience
Domain: Life Sciences
Open Access status: bronze
OpenAlex ID: W4409865372