Machine Learning in MRI Brain Imaging: A Review of Methods, Challenges, and Future Directions.

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Neurology

Background:

• Manual analysis of brain Magnetic Resonance Imaging (MRI) is time-consuming and variable.
• Machine learning (ML) offers automated solutions for MRI analysis, particularly for segmentation and classification.
• Brain tumor classification and segmentation are critical diagnostic tasks in neuro-oncology.

Purpose of the Study:

• To provide an updated narrative review of ML applications in brain MRI analysis.
• To focus on ML-driven tumor classification and segmentation using MRI data.
• To analyze trends and performance of ML models in brain tumor imaging from 2020-2025.

Main Methods:

• A comprehensive literature search was performed in PubMed, Scopus, and Mendeley Catalog.
• Inclusion criteria focused on original English research articles (Jan 2020-Apr 2025) using ML for brain tumor classification/segmentation on MRI with validation.
• 108 studies were qualitatively analyzed, excluding animal models, non-imaging data, and studies lacking validation.

Main Results:

• Convolutional Neural Networks (CNNs) dominate brain MRI analysis, achieving classification accuracies of 95-99% and Dice scores of 0.83-0.94 for segmentation.
• Hybrid models (CNN-SVM, CNN-LSTM) and Transformer-based models (e.g., Swin Transformer) demonstrated superior performance, with accuracies up to 99.9%.
• Transfer learning and data augmentation were common strategies to address data limitations; radiomics emerged for personalized diagnostics.

Conclusions:

• ML, particularly deep learning models like CNNs and Transformers, shows immense potential in improving brain MRI analysis for tumor detection and delineation.
• Despite high reported accuracies, challenges include overfitting, generalization to diverse clinical data, and the need for standardized evaluation protocols.
• Further research focusing on rigorous clinical validation and benchmarking is essential for the successful integration of ML into routine clinical practice.