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

A brain tumor is the result of abnormal growth of cells in the central nervous system (CNS), widely considered as a complex and diverse clinical entity that is difficult to diagnose and cure. In this study, we focus on current advances in medical imaging, particularly magnetic resonance imaging (MRI...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Vol. 25; no. 9; p. 2746
Main Authors: Missaoui, Rim, Hechkel, Wided, Saadaoui, Wajdi, Helali, Abdelhamid, Leo, Marco
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 26.04.2025
MDPI
Subjects:
Accuracy
Algorithms
Artificial intelligence
Automation
Brain - diagnostic imaging
Brain cancer
Brain Neoplasms - diagnostic imaging
Brain tumors
Classification
Decision making
Deep Learning
deep learning (DL)
Glioma
Humans
Machine Learning
machine learning (ML)
Magnetic resonance imaging
magnetic resonance imaging (MRI)
Magnetic Resonance Imaging - methods
Medical imaging
Medical imaging equipment
Medical research
Medicine, Experimental
Metastasis
NMR
Nuclear magnetic resonance
Radiation
Radiomics
Review
Spinal cord
Tomography
Tumors
Switzerland
magnetic resonance imaging (MRI)
machine learning (ML)
brain tumors
deep learning (DL)
ISSN:1424-8220, 1424-8220
Online Access:Get full text
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Summary:A brain tumor is the result of abnormal growth of cells in the central nervous system (CNS), widely considered as a complex and diverse clinical entity that is difficult to diagnose and cure. In this study, we focus on current advances in medical imaging, particularly magnetic resonance imaging (MRI), and how machine learning (ML) and deep learning (DL) algorithms might be combined with clinical assessments to improve brain tumor diagnosis. Due to its superior contrast resolution and safety compared to other imaging methods, MRI is highlighted as the preferred imaging modality for brain tumors. The challenges related to brain tumor analysis in different processes including detection, segmentation, classification, and survival prediction are addressed along with how ML/DL approaches significantly improve these steps. We systematically analyzed 107 studies (2018–2024) employing ML, DL, and hybrid models across publicly available datasets such as BraTS, TCIA, and Figshare. In the light of recent developments in brain tumor analysis, many algorithms have been proposed to accurately obtain ontological characteristics of tumors, enhancing diagnostic precision and personalized therapeutic strategies.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s25092746