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A Comprehensive Survey on Brain Tumor Diagnosis Using Deep Learning and Emerging Hybrid Techniques with Multi-modal MR Image

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Abstract

The brain tumor is considered the deadly disease of the century. At present, neuroscience and artificial intelligence conspire in the timely delineation, detection, and classification of brain tumors. The process of manually classifying and segmenting many volumes of MRI scans is a challenging and laborious task. Therefore, there is an essential requirement to build computer-aided diagnosis systems to diagnose brain tumors timely. Herein review focuses on the advances of the last decade in brain tumor segmentation, feature extraction, and classification through powerful and versatile brain imaging modality Magnetic Resonance Imaging (MRI). However, particular emphasis on deep learning and hybrid techniques. We have summarized the work of researchers published in the last decade (2010–2019) termed as the 10s and the present decade (only including the year 2020) termed as the 20s. The decades in review reveal the bore witness to the critical revolutionary paradigm shift in artificial intelligence viz. conventional/machine learning methods, emerged deep learning, and emerging hybrid techniques. This review also covers some persistent concerns on using the type of classifier and striking trends in commonly employed MRI modalities for brain tumor diagnosis. Moreover, this study ensures the limitation, solutions, and future trends or opens up the researchers’ advanced challenges to develop an efficient system exhibiting clinically acceptable accuracy that assists the radiologists for the brain tumor prognosis.

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Funding

This study is supported by the National Key R&D Program of China with Project No. 2020YFB2104402.

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Authors and Affiliations

  1. Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China

    Saqib Ali, Jianqiang Li, Khalil ur Rehman & Tariq Mahmood

  2. Computer Science Division, University of Aizu, Aizuwakamatsu, 965-8580, Japan

    Yan Pei

  3. Department of Chemistry and Chemical Engineering, Beijing University of Technology, Beijing, 100124, China

    Rooha Khurram

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  1. Saqib Ali
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  2. Jianqiang Li
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  3. Yan Pei
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  4. Rooha Khurram
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  5. Khalil ur Rehman
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  6. Tariq Mahmood
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Each author took part in the present work conception and/or design. Tasks of data collection, material preparation, data analysis, and writing of the original draft were executed by Saqib Ali. Jianqiang Li and Yan Pei supervise this study. Rooha Khurram and Khalil ur Rehman helped in reviewing, and editing the manuscript. All authors read and approved the final manuscript.

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Correspondence to Yan Pei.

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Ali, S., Li, J., Pei, Y. et al. A Comprehensive Survey on Brain Tumor Diagnosis Using Deep Learning and Emerging Hybrid Techniques with Multi-modal MR Image. Arch Computat Methods Eng 29, 4871–4896 (2022). https://doi.org/10.1007/s11831-022-09758-z

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