Advanced Deep Learning and Machine Learning Techniques for MRI Brain Tumor Analysis: A Review.

Area of Science:

• Neuro-oncology
• Medical Imaging
• Artificial Intelligence

Background:

• Brain tumors are complex CNS cell growths, challenging to diagnose and treat.
• Magnetic Resonance Imaging (MRI) is the preferred modality due to its safety and contrast resolution.
• Accurate diagnosis is crucial for effective, personalized treatment strategies.

Purpose of the Study:

• To review advances in using machine learning (ML) and deep learning (DL) with medical imaging for brain tumor diagnosis.
• To explore how ML/DL algorithms improve various stages of brain tumor analysis.
• To assess the impact of these technologies on diagnostic precision and treatment planning.

Main Methods:

• Systematic analysis of 107 studies published between 2018 and 2024.
• Focus on studies utilizing ML, DL, and hybrid models for brain tumor analysis.
• Inclusion of research using public datasets like BraTS, TCIA, and Figshare.

Main Results:

• ML and DL algorithms show significant improvements in brain tumor detection, segmentation, classification, and survival prediction.
• Advanced algorithms accurately identify tumor characteristics, aiding diagnostic precision.
• The integration of ML/DL with MRI enhances diagnostic capabilities.

Conclusions:

• ML and DL, particularly with MRI, offer powerful tools for advancing brain tumor diagnosis.
• These AI-driven approaches are key to enhancing diagnostic accuracy and enabling personalized therapeutic strategies.
• Continued research in this area promises further improvements in neuro-oncology care.