Deep Learning Approaches for Brain Tumor Detection and Classification Using MRI Images (2020 to 2024): A Systematic Review.
Brain tumor is a type of disease caused by uncontrolled cell proliferation in the brain leading to serious health issues such as memory loss and motor impairment. Therefore, early diagnosis of brain tumors plays a crucial role to extend the survival of patients. However, given the busy nature of the...
| Publicado en: | Journal of Imaging Informatics in Medicine Vol. 38; no. 3; pp. 1403 - 1434 |
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| Autores principales: | , |
| Formato: | algorithm diagnostic images pictorial research systematic review tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=185280524&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 185280524 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Jun2025 vid: 38 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 185280524 185280524 185280524 10.1007/s10278-024-01283-8 185280524 ppf: 1403 ppct: 31 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Deep Learning Approaches for Brain Tumor Detection and Classification Using MRI Images (2020 to 2024): A Systematic Review. aug: au: Bouhafra, Sara El Bahi, Hassan affil: https://ror.org/04xf6nm78 Faculty of Sciences and Techniques, Department of Computer Science, L2IS Laboratory, Cadi Ayyad University, Marrakesh, Morocco sug: subj: Deep Learning Utilization Brain Neoplasms Classification Brain Neoplasms Diagnosis Magnetic Resonance Imaging Methods Diagnosis, Computer Assisted Image Interpretation, Computer Assisted Methods Human Systematic Review Autoencoder Convolutional Neural Networks Machine Learning Generative Adversarial Networks ab: Brain tumor is a type of disease caused by uncontrolled cell proliferation in the brain leading to serious health issues such as memory loss and motor impairment. Therefore, early diagnosis of brain tumors plays a crucial role to extend the survival of patients. However, given the busy nature of the work of radiologists and aiming to reduce the likelihood of false diagnoses, advancing technologies including computer-aided diagnosis and artificial intelligence have shown an important role in assisting radiologists. In recent years, a number of deep learning-based methods have been applied for brain tumor detection and classification using MRI images and achieved promising results. The main objective of this paper is to present a detailed review of the previous researches in this field. In addition, This work summarizes the existing limitations and significant highlights. The study systematically reviews 60 articles researches published between 2020 and January 2024, extensively covering methods such as transfer learning, autoencoders, transformers, and attention mechanisms. The key findings formulated in this paper provide an analytic comparison and future directions. The review aims to provide a comprehensive understanding of automatic techniques that may be useful for professionals and academic communities working on brain tumor classification and detection. pubtype: Academic Journal doctype: algorithm diagnostic images pictorial research systematic review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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