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<p>Brain tumors (BrTs), as one of the most complex and challenging intracranial neoplasms, are of particular importance in medical science due to their aggressive growth and resistance to treatment. Early detection of tumors and their type in the early stages is critical. Machine learning (ML) techniques, as one of the most advanced artificial intelligence (AI) technologies, have considerable potential for the development of BrT therapy and detection processes. However, there are several challenges in utilizing ML in the medical detection and therapy of BrTs. Limitations related to insufficient training data, data heterogeneity, interpretability of deep learning (DL) models, ethical challenges, and patient data privacy are among the key obstacles to the widespread implementation of these techniques in clinical settings. This work provides a comprehensive review of the applications of ML in diagnosing and treating BrTs. This paper systematically reviews the methods of DL, federated learning (FL), reinforcement learning (RL), and hybrid models used in detecting and treating BrTs by providing a detailed taxonomy and a comprehensive classification of these technologies. The main objective of this research is to evaluate the effectiveness of ML systems in improving diagnostic and therapeutic processes. Analysis of the application of these techniques in the therapy and detection of BrTs shows that the main focus of these studies is on precise tumor detection (PTD) with 32% and efficient computational processing (ECP) with 22%. These findings indicate that improving PTD and ECP should be the main priority in developing ML systems for diagnosing and treating BrTs. The results of this research emphasize that using ML can lead to increased accuracy in medical image analysis, reduced human errors, improved performance of predictive models, and optimized clinical decision-making. Finally, this study paves the way for the effective integration of ML techniques in identifying and curing BrTs By evaluating existing issues and proposing enhancement strategies.</p>

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Advancements in Machine Learning for Brain Tumor Classification and Diagnosis: A Comprehensive Review of Challenges and Future Directions

  • 期刊论文

  • 发表日期：

    2025年9月3日

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  • Mohsen Ghorbian,
  • Saeid Ghorbian,
  • Mostafa Ghobaei-Arani

摘要

Brain tumors (BrTs), as one of the most complex and challenging intracranial neoplasms, are of particular importance in medical science due to their aggressive growth and resistance to treatment. Early detection of tumors and their type in the early stages is critical. Machine learning (ML) techniques, as one of the most advanced artificial intelligence (AI) technologies, have considerable potential for the development of BrT therapy and detection processes. However, there are several challenges in utilizing ML in the medical detection and therapy of BrTs. Limitations related to insufficient training data, data heterogeneity, interpretability of deep learning (DL) models, ethical challenges, and patient data privacy are among the key obstacles to the widespread implementation of these techniques in clinical settings. This work provides a comprehensive review of the applications of ML in diagnosing and treating BrTs. This paper systematically reviews the methods of DL, federated learning (FL), reinforcement learning (RL), and hybrid models used in detecting and treating BrTs by providing a detailed taxonomy and a comprehensive classification of these technologies. The main objective of this research is to evaluate the effectiveness of ML systems in improving diagnostic and therapeutic processes. Analysis of the application of these techniques in the therapy and detection of BrTs shows that the main focus of these studies is on precise tumor detection (PTD) with 32% and efficient computational processing (ECP) with 22%. These findings indicate that improving PTD and ECP should be the main priority in developing ML systems for diagnosing and treating BrTs. The results of this research emphasize that using ML can lead to increased accuracy in medical image analysis, reduced human errors, improved performance of predictive models, and optimized clinical decision-making. Finally, this study paves the way for the effective integration of ML techniques in identifying and curing BrTs By evaluating existing issues and proposing enhancement strategies.