Deep Learning for Multigrade Brain Tumor Classification in Smart Healthcare Systems: A Prospective Survey

Simple item page
dc.contributor.authorMuhammad, Khan
dc.contributor.authorKhan, Salman
dc.contributor.authorSer, Javier Del
dc.contributor.authorAlbuquerque, Victor Hugo C.De
dc.contributor.institutionIA
dc.date.issued2021-02
dc.descriptionPublisher Copyright: © 2012 IEEE.
dc.description.abstractBrain tumor is one of the most dangerous cancers in people of all ages, and its grade recognition is a challenging problem for radiologists in health monitoring and automated diagnosis. Recently, numerous methods based on deep learning have been presented in the literature for brain tumor classification (BTC) in order to assist radiologists for a better diagnostic analysis. In this overview, we present an in-depth review of the surveys published so far and recent deep learning-based methods for BTC. Our survey covers the main steps of deep learning-based BTC methods, including preprocessing, features extraction, and classification, along with their achievements and limitations. We also investigate the state-of-the-art convolutional neural network models for BTC by performing extensive experiments using transfer learning with and without data augmentation. Furthermore, this overview describes available benchmark data sets used for the evaluation of BTC. Finally, this survey does not only look into the past literature on the topic but also steps on it to delve into the future of this area and enumerates some research directions that should be followed in the future, especially for personalized and smart healthcare.en
dc.description.sponsorshipManuscript received June 6, 2019; revised December 2, 2019 and March 5, 2020; accepted May 16, 2020. Date of publication June 30, 2020; date of current version February 4, 2021. This work was supported in part by the Brazilian National Council for Research and Development (CNPq) under Grant 304315/2017-6 and Grant 430274/2018-1. The work of Javier Del Ser was supported in part by the Basque Government through the EMAITEK and ELKARTEK Funding Programs and in part by the Consolidated Research Group MATHMODE through the Department of Education of the Basque Government under Grant IT1294-19. (Corresponding author: Khan Muhammad.) Khan Muhammad is with the Department of Software, Sejong University, Seoul 143-747, South Korea (e-mail: khan.muhammad@ieee.org).
dc.description.statusPeer reviewed
dc.format.extent16
dc.identifier.citationMuhammad , K , Khan , S , Ser , J D & Albuquerque , V H C D 2021 , ' Deep Learning for Multigrade Brain Tumor Classification in Smart Healthcare Systems : A Prospective Survey ' , IEEE Transactions on Neural Networks and Learning Systems , vol. 32 , no. 2 , 9129779 , pp. 507-522 . https://doi.org/10.1109/TNNLS.2020.2995800
dc.identifier.doi10.1109/TNNLS.2020.2995800
dc.identifier.issn2162-237X
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85100723593&partnerID=8YFLogxK
dc.language.isoeng
dc.relation.ispartofIEEE Transactions on Neural Networks and Learning Systems
dc.relation.projectIDEusko Jaurlaritza, IT1294-19
dc.relation.projectIDConselho Nacional de Desenvolvimento Científico e Tecnológico, CNPq, 430274/2018-1-304315/2017-6
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.subject.keywordsArtificial intelligence
dc.subject.keywordsbiomedical data analysis
dc.subject.keywordsbrain tumor classification (BTC)
dc.subject.keywordsdeep learning
dc.subject.keywordshealth monitoring
dc.subject.keywordssmart healthcare
dc.subject.keywordstransfer learning
dc.subject.keywordsSoftware
dc.subject.keywordsComputer Science Applications
dc.subject.keywordsComputer Networks and Communications
dc.subject.keywordsArtificial Intelligence
dc.subject.keywordsSDG 3 - Good Health and Well-being
dc.titleDeep Learning for Multigrade Brain Tumor Classification in Smart Healthcare Systems: A Prospective Surveyen
dc.typejournal article
Files