Challenges and Advances in Classifying Brain Tumors: An Overview of Machine, Deep Learning, and Hybrid Approaches with Future Perspectives in Medical Imaging
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View abstract on PubMed
Summary
This summary is machine-generated.Machine learning (ML) and deep learning (DL) significantly improve brain tumor classification accuracy using MRI scans. Continued research is vital to overcome challenges and integrate these AI tools into clinical practice for better patient outcomes.
Area Of Science
- Neuro-oncology
- Artificial Intelligence
- Medical Imaging
Background
- Accurate brain tumor classification is crucial for effective neuro-oncology treatment and patient outcomes.
- Magnetic Resonance Imaging (MRI) is a key noninvasive technique for detecting and characterizing brain tumors.
- Machine learning (ML) and deep learning (DL) offer advanced methods for analyzing medical imaging data.
Purpose Of The Study
- To comprehensively review ML and DL models for brain tumor classification using MRI.
- To analyze the effectiveness and limitations of various ML and DL approaches.
- To identify challenges and propose future directions for AI in brain tumor diagnosis.
Main Methods
- Review of diverse ML algorithms, including Support Vector Machines (SVM) and Decision Trees.
- Analysis of deep learning models such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN).
- Comparative evaluation of studies using metrics like accuracy, sensitivity, specificity, and AUC-ROC.
Main Results
- ML and DL models demonstrate enhanced accuracy and efficiency in brain tumor classification.
- Various algorithms show different strengths and weaknesses across datasets.
- Key challenges include data scarcity, computational demands, interpretability, and clinical integration.
Conclusions
- ML and DL hold transformative potential for advancing brain tumor diagnosis.
- Future directions include multi-modal imaging, explainable AI, and privacy-preserving techniques.
- Continued research is essential for successful clinical implementation and improved patient care.
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