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

• Published in: Magnetic resonance imaging Vol. 61; pp. 300 - 318
• Main Authors: Abd-Ellah, Mahmoud Khaled, Awad, Ali Ismail, Khalaf, Ashraf A.M., Hamed, Hesham F.A.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier Inc 01.09.2019
• Subjects: Algorithms; Brain - diagnostic imaging; Brain - pathology; Brain Neoplasms - diagnostic imaging; Brain Neoplasms - pathology; Brain tumor diagnosis; Computer-aided methods; Deep learning techniques; Diagnosis, Computer-Assisted - methods; Glioma - diagnostic imaging; Glioma - pathology; Humans; Image Processing, Computer-Assisted - methods; Information systems; Informationssystem; Machine Learning; Magnetic Resonance Imaging; MRI images; Traditional machine learning techniques; Tumor classification; Tumor detection; Tumor segmentation; Brain tumor diagnosis; Traditional machine learning techniques; Computer-aided methods; Deep learning techniques; Tumor segmentation; MRI images; Tumor detection; Tumor classification
• ISSN: 0730-725X; 1873-5894; 1873-5894
• DOI: 10.1016/j.mri.2019.05.028

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.