A review on brain tumor diagnosis from MRI images: Practical implications, key achievements, and lessons learned

Mahmoud Khaled Abd-Ellah, Ali Ismail Awad, Ashraf A.M. Khalaf, Hesham F.A. Hamed

Research output: Contribution to journalReview articlepeer-review

191 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)300-318
Number of pages19
JournalMagnetic Resonance Imaging
Volume61
DOIs
Publication statusPublished - Sept 2019
Externally publishedYes

Keywords

  • Brain tumor diagnosis
  • Computer-aided methods
  • Deep learning techniques
  • MRI images
  • Traditional machine learning techniques
  • Tumor classification
  • Tumor detection
  • Tumor segmentation

ASJC Scopus subject areas

  • Biophysics
  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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