Brain tumor segmentation of MRI images: a comprehensive review on the application of artificial intelligence tools
Yükleniyor...
Tarih
2023
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Background: Brain cancer is a destructive and life-threatening disease that imposes immense negative effects on patients’ lives. Therefore, the detection of brain tumors at an early stage improves the impact of treatments and increases the patients survival rates. 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. © 2022 Elsevier Ltd
Açıklama
Anahtar Kelimeler
Artificial Intelligence, Brain Tumor, MRI Modalities, Tumor Classification, Tumor Segmentation
Kaynak
Computers in Biology and Medicine
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
Sayı
Künye
Ranjbarzadeh, R., Caputo, A., Tirkolaee, E. B., Ghoushchi, S. J., & Bendechache, M. (2022). Brain tumor segmentation of MRI images: A comprehensive review on the application of artificial intelligence tools. Computers in Biology and Medicine, 106405.
