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Brain tumor segmentation of MRI images: a comprehensive review on the application of artificial intelligence tools

Ranjbarzadeh, Ramin orcid logoORCID: 0000-0001-7065-9060, Caputo, Annalina orcid logoORCID: 0000-0002-7144-8545, Jafarzadeh Ghoushchi, Saeid orcid logoORCID: 0000-0003-3665-9010, Babaee Tirkolaee, Erfan orcid logoORCID: 0000-0003-1664-9210 and Bendechache, Malika orcid logoORCID: 0000-0003-0069-1860 (2022) Brain tumor segmentation of MRI images: a comprehensive review on the application of artificial intelligence tools. Computers in Biology and Medicine, 152 . ISSN 0010-4825

Abstract
Background Brain cancer is a destructive and life-threatening disease that imposes immense negative effects on patients’ lives. Therefore, detection of brain tumors at an early stage improves the impact of treatments and increases patients' survival rate. However, detecting brain tumors in their initial stages is a demanding task and an unmet need. Methods The present study presents a comprehensive review of the recent Artificial Intelligence (AI) methods of diagnosing brain tumors using MRI images. These AI techniques can be divided into Supervised, Unsupervised, and Deep learning (DL) methods. Results Diagnosing and segmenting brain tumors usually begin with Magnetic Resonance Imaging (MRI) on the brain since MRI is a noninvasive imaging technique. Another existing challenge is that the growth of technology is faster than the rate of increase in the number of medical staff who can employ these technologies. It has resulted in an increased risk of diagnostic misinterpretation. Therefore, developing robust automated brain tumor detection techniques has been studied widely over the past years. Conclusion The current review provides an analysis of the performance of modern methods in this area. Moreover, various image segmentation methods in addition to the recent efforts of researchers are summarized. Finally, the paper discusses open questions and suggests directions for future research.
Metadata
Item Type:Article (Published)
Refereed:Yes
Uncontrolled Keywords:Brain Tumor; Artificial Intelligence; Tumor Segmentation; Tumor Classification; MRI Modalities
Subjects:Computer Science > Artificial intelligence
Computer Science > Machine learning
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Publisher:Elsevier
Official URL:https://doi.org/10.1016/j.compbiomed.2022.106405
Copyright Information:© 2022 Elsevier
Funders:Science Foundation Ireland under grant number no. 18/CRT/6183, Science Foundation Ireland under grant number no. (grant 13/RC/2106/_P2) ADAPT &(grant 13/RC/2094/_P2) LERO, European Regional Development Fund
ID Code:28045
Deposited On:23 Jan 2023 13:50 by Ramin Ranjbarzadeh Kondrood . Last Modified 10 Dec 2024 04:30
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