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      Brain Tumor Diagnosis Using Machine Learning, Convolutional Neural Networks, Capsule Neural Networks and Vision Transformers, Applied to MRI: A Survey.

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          Abstract

          Management of brain tumors is based on clinical and radiological information with presumed grade dictating treatment. Hence, a non-invasive assessment of tumor grade is of paramount importance to choose the best treatment plan. Convolutional Neural Networks (CNNs) represent one of the effective Deep Learning (DL)-based techniques that have been used for brain tumor diagnosis. However, they are unable to handle input modifications effectively. Capsule neural networks (CapsNets) are a novel type of machine learning (ML) architecture that was recently developed to address the drawbacks of CNNs. CapsNets are resistant to rotations and affine translations, which is beneficial when processing medical imaging datasets. Moreover, Vision Transformers (ViT)-based solutions have been very recently proposed to address the issue of long-range dependency in CNNs. This survey provides a comprehensive overview of brain tumor classification and segmentation techniques, with a focus on ML-based, CNN-based, CapsNet-based, and ViT-based techniques. The survey highlights the fundamental contributions of recent studies and the performance of state-of-the-art techniques. Moreover, we present an in-depth discussion of crucial issues and open challenges. We also identify some key limitations and promising future research directions. We envisage that this survey shall serve as a good springboard for further study.

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          Author and article information

          Journal
          J Imaging
          Journal of imaging
          MDPI AG
          2313-433X
          2313-433X
          Jul 22 2022
          : 8
          : 8
          Affiliations
          [1 ] NOVA Information Management School (NOVA IMS), Universidade NOVA de Lisboa, Campus de Campolide, 1070-312 Lisboa, Portugal.
          [2 ] Department of Computer Science and Informatics, University of the Free State, Phuthaditjhaba 9866, South Africa.
          [3 ] Department of Biomedical and Neuromotor Sciences, Alma Mater Studiorum-University of Bologna, 40138 Bologna, Italy.
          [4 ] IRCCS Istituto delle Scienze Neurologiche di Bologna, Functional and Molecular Neuroimaging Unit, 40139 Bologna, Italy.
          [5 ] Department of Physiology, Anatomy, and Genetics, University of Oxford, Oxford OX1 3PT, UK.
          [6 ] Department of Radiology, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK.
          [7 ] Oxford Centre for Clinical Magnetic Research Imaging, University of Oxford, Oxford OX3 9DU, UK.
          [8 ] Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham B15 2SY, UK.
          [9 ] Department of Information and Electrical Engineering and Applied Mathematics, University of Salerno, 84084 Fisciano, Italy.
          Article
          jimaging8080205
          10.3390/jimaging8080205
          9331677
          35893083
          3013d6b0-c17a-43e7-be3d-ff41356281bb
          History

          deep learning,vision transformers,magnetic resonance imaging,machine learning,capsule neural networks,brain cancer

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