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Optimization-Driven Hybrid Machine Learning Framework for Brain Tumor Classification in MRI with Metaheuristic

Yasin Özkan1, Yusuf Bahri Özçelik2, Aytaç Altan2

  • 1Department of Computer Technologies, Zonguldak Bülent Ecevit University, Zonguldak 67100, Türkiye.

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This study introduces an optimized hybrid machine learning model for accurate brain tumor classification using magnetic resonance imaging (MRI). The framework significantly improves diagnostic accuracy and reduces computational load for computer-aided diagnosis systems.

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brain tumor classificationhybrid machine learning frameworkmagnetic resonance imagingmetaheuristic feature selectionsuperb fairy-wren optimization

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Area of Science:

  • Medical Imaging and Diagnostics
  • Artificial Intelligence in Healthcare
  • Machine Learning for Medical Applications

Background:

  • Brain tumors pose significant diagnostic challenges due to variations in size, morphology, and location.
  • Manual interpretation of Magnetic Resonance Imaging (MRI) is time-consuming, subjective, and prone to errors.
  • Accurate and efficient automated brain tumor classification is crucial for timely diagnosis and treatment.

Purpose of the Study:

  • To develop an optimization-driven hybrid machine learning framework for accurate and computationally efficient automatic brain tumor classification.
  • To enhance diagnostic performance by combining automated tumor localization, image standardization, and optimized feature selection.

Main Methods:

  • Utilized a dataset of 834 MRI images for training, validation, and testing.
  • Employed YOLOv11 for automated tumor region localization and image standardization (Gaussian noise reduction, bilinear interpolation).
  • Extracted 39 entropy-based features and applied the Superb Fairy-Wren Optimization Algorithm (SFOA) for feature selection, comparing it with PSO, HHO, and PO.
  • Performed final classification using k-Nearest Neighbors (kNN) and Support Vector Machines (SVM).

Main Results:

  • YOLOv11 achieved high performance in tumor detection (98.87% mAP@50).
  • SFOA reduced feature dimensionality from 39 to 5, achieving 99.20% classification accuracy with kNN.
  • The SFOA-kNN model outperformed other optimization algorithms and SVM, demonstrating superior diagnostic accuracy and efficiency.

Conclusions:

  • The proposed framework combining entropy-based features, SFOA feature selection, and kNN classification significantly enhances brain tumor diagnostic accuracy.
  • The approach reduces computational complexity, making it suitable for integration into computer-aided diagnosis systems.
  • This method shows strong potential to support clinical decision-making in neuro-oncology.