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In the field of glioblastoma diagnostics, accurately characterizing tumors is a significant challenge due to the aggressive nature and common occurrence of the condition. Radiomics, known for its quantitative imaging analysis, stands out as a critical tool for exploring tumor heterogeneity, which is key to designing effective treatment plans. Our research focuses on comparing different radiomics feature selection methods, including Random Forest, Correlation-Based, Mutual Information, L1 Regularization (Lasso), and XGBoost, alongside assessing the performance of classifiers like K-Nearest Neighbor, Quadratic Discriminant Analysis, and Multilayer Perceptron. Through detailed evaluation of these methods and classifiers, our aim is to identify the best combinations that enhance diagnostic accuracy and dependability. This effort is directed towards facilitating the creation of personalized treatment approaches, aiming to improve treatment results for people facing glioblastoma.

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Machine Learning Approaches for Brain Tumor Classification in Multimodal MR Images

  • 章节

  • 发表日期：

    2025年

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  • V. L. Sowmya,
  • A. Bharathi Malakreddy,
  • Santhi Natarajan,
  • N. Prathik,
  • S. K. Manjunath

摘要

In the field of glioblastoma diagnostics, accurately characterizing tumors is a significant challenge due to the aggressive nature and common occurrence of the condition. Radiomics, known for its quantitative imaging analysis, stands out as a critical tool for exploring tumor heterogeneity, which is key to designing effective treatment plans. Our research focuses on comparing different radiomics feature selection methods, including Random Forest, Correlation-Based, Mutual Information, L1 Regularization (Lasso), and XGBoost, alongside assessing the performance of classifiers like K-Nearest Neighbor, Quadratic Discriminant Analysis, and Multilayer Perceptron. Through detailed evaluation of these methods and classifiers, our aim is to identify the best combinations that enhance diagnostic accuracy and dependability. This effort is directed towards facilitating the creation of personalized treatment approaches, aiming to improve treatment results for people facing glioblastoma.