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Brain tumor detection in MRI scans is critical for early diagnosis and treatment planning. This paper proposes an AI-driven framework that integrates image processing and machine learning to improve tumor identification accuracy. The proposed method applies preprocessing (skull stripping, contrast enhancement), template matching, and probabilistic classification (Naïve Bayes, Decision Trees) for multi-class tumor localization. Extensive experiments on the REMBRANDT dataset demonstrate the method’s efficacy, achieving up to 75% accuracy. These results highlight the system’s potential for assisting radiologists in routine diagnostics.

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AI-Driven Tumor Detection in Brain MRI Using Machine Learning and Image Processing

  • 会议论文

  • 发表日期：

    2026年

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  • Kushaghra Sharma,
  • Sandeep Reddy

摘要

Brain tumor detection in MRI scans is critical for early diagnosis and treatment planning. This paper proposes an AI-driven framework that integrates image processing and machine learning to improve tumor identification accuracy. The proposed method applies preprocessing (skull stripping, contrast enhancement), template matching, and probabilistic classification (Naïve Bayes, Decision Trees) for multi-class tumor localization. Extensive experiments on the REMBRANDT dataset demonstrate the method’s efficacy, achieving up to 75% accuracy. These results highlight the system’s potential for assisting radiologists in routine diagnostics.