期刊
MAGNETIC RESONANCE IMAGING
卷 61, 期 -, 页码 300-318出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.mri.2019.05.028
关键词
Brain tumor diagnosis; Computer-aided methods; MRI images; Tumor detection; Tumor segmentation; Tumor classification; Traditional machine learning techniques; Deep learning techniques
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.
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