RT Journal Article
T1 Deep Learning for Multigrade Brain Tumor Classification in Smart Healthcare Systems: 												A Prospective Survey
A1 Muhammad, Khan
A1 Khan, Salman
A1 Ser, Javier Del
A1 Albuquerque, Victor Hugo C.De
AB Brain 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.
SN 2162-237X
YR 2021
FD 2021-02
LA eng
NO Muhammad , 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
NO Publisher Copyright: © 2012 IEEE.
NO Manuscript 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).
DS TECNALIA Publications
RD 28 sept 2024