Deep Learning for Multigrade Brain Tumor Classification in Smart Healthcare Systems: A Prospective Survey
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 classifica...
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Veröffentlicht in: | IEEE transaction on neural networks and learning systems 2021-02, Vol.32 (2), p.507-522 |
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description | 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. |
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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.</description><identifier>ISSN: 2162-237X</identifier><identifier>EISSN: 2162-2388</identifier><identifier>DOI: 10.1109/TNNLS.2020.2995800</identifier><identifier>PMID: 32603291</identifier><identifier>CODEN: ITNNAL</identifier><language>eng</language><publisher>United States: IEEE</publisher><subject>Artificial Intelligence ; Artificial neural networks ; Benchmarking ; Biological system modeling ; biomedical data analysis ; Brain ; Brain cancer ; Brain Neoplasms - classification ; Brain Neoplasms - diagnostic imaging ; brain tumor classification (BTC) ; Brain tumors ; Classification ; Deep Learning ; Delivery of Health Care ; Diagnostic systems ; Feature extraction ; Health care ; health monitoring ; Humans ; Image Processing, Computer-Assisted ; Image segmentation ; Machine learning ; Magnetic Resonance Imaging ; Medical diagnostic imaging ; Neural networks ; Neural Networks, Computer ; Polls & surveys ; Prospective Studies ; Radiologists ; smart healthcare ; Surveys and Questionnaires ; Tomography, X-Ray Computed ; Transfer learning ; Transfer, Psychology ; Tumors</subject><ispartof>IEEE transaction on neural networks and learning systems, 2021-02, Vol.32 (2), p.507-522</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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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. 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subjects | Artificial Intelligence Artificial neural networks Benchmarking Biological system modeling biomedical data analysis Brain Brain cancer Brain Neoplasms - classification Brain Neoplasms - diagnostic imaging brain tumor classification (BTC) Brain tumors Classification Deep Learning Delivery of Health Care Diagnostic systems Feature extraction Health care health monitoring Humans Image Processing, Computer-Assisted Image segmentation Machine learning Magnetic Resonance Imaging Medical diagnostic imaging Neural networks Neural Networks, Computer Polls & surveys Prospective Studies Radiologists smart healthcare Surveys and Questionnaires Tomography, X-Ray Computed Transfer learning Transfer, Psychology Tumors |
title | Deep Learning for Multigrade Brain Tumor Classification in Smart Healthcare Systems: A Prospective Survey |
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