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A Review on Brain Tumor Diagnosis from MRI Images: Practical Implications, Key Achievements, and Lessons Learned
Electronics and Communications Department, Al-Madina Higher Institute for Engineering and Technology, Giza, Egypt.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. Faculty of Engineering, Al-Azhar University, Qena, Egypt.ORCID iD: 0000-0002-3800-0757
Electronics and Communications Department, Faculty of Engineering, Minia University, Minia, Egypt.
Electronics and Communications Department, Faculty of Engineering, Minia University, Minia, Egypt.
2019 (English)In: Magnetic Resonance Imaging, ISSN 0730-725X, E-ISSN 1873-5894, Vol. 61, p. 300-318Article in journal (Refereed) Published
Abstract [en]

The successful early diagnosis of brain tumors plays a major role in improving the treatment outcomes and thus improving patient survival. Manually evaluating the numerous magnetic resonance imaging (MRI) images produced routinely in the clinic is a difficult process. Thus, there is a crucial need for computer-aided methods with better accuracy for early tumor diagnosis. Computer-aided brain tumor diagnosis from MRI images consists of tumor detection, segmentation, and classification processes. Over the past few years, many studies have focused on traditional or classical machine learning techniques for brain tumor diagnosis. Recently, interest has developed in using deep learning techniques for diagnosing brain tumors with better accuracy and robustness. This study presents a comprehensive review of traditional machine learning techniques and evolving deep learning techniques for brain tumor diagnosis. This review paper identifies the key achievements reflected in the performance measurement metrics of the applied algorithms in the three diagnosis processes. In addition, this study discusses the key findings and draws attention to the lessons learned as a roadmap for future research.

Place, publisher, year, edition, pages
Elsevier, 2019. Vol. 61, p. 300-318
Keywords [en]
Brain tumor diagnosis, Computer-aided methods, MRI images, Tumor detection, Tumor segmentation, Tumor classification, Traditional machine learning techniques, Deep learning techniques
National Category
Information Systems, Social aspects
Research subject
Information systems
Identifiers
URN: urn:nbn:se:ltu:diva-74505DOI: 10.1016/j.mri.2019.05.028ISI: 000479327400035PubMedID: 31173851Scopus ID: 2-s2.0-85067489010OAI: oai:DiVA.org:ltu-74505DiVA, id: diva2:1324427
Note

Validerad;2019;Nivå 2;2019-06-03 (svasva)

Available from: 2019-06-13 Created: 2019-06-13 Last updated: 2019-08-29Bibliographically approved

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Awad, Ali Ismail

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