The Application of Deep Convolutional Neural Networks to Brain Cancer Images: A Survey
In recent years, improved deep learning techniques have been applied to biomedical image processing for the classification and segmentation of different tumors based on magnetic resonance imaging (MRI) and histopathological imaging (H&E) clinical information. Deep Convolutional Neural Networks (...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-11-01
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| Series: | Journal of Personalized Medicine |
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| Online Access: | https://www.mdpi.com/2075-4426/10/4/224 |
| _version_ | 1797548029699424256 |
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| author | Amin Zadeh Shirazi Eric Fornaciari Mark D. McDonnell Mahdi Yaghoobi Yesenia Cevallos Luis Tello-Oquendo Deysi Inca Guillermo A. Gomez |
| author_facet | Amin Zadeh Shirazi Eric Fornaciari Mark D. McDonnell Mahdi Yaghoobi Yesenia Cevallos Luis Tello-Oquendo Deysi Inca Guillermo A. Gomez |
| author_sort | Amin Zadeh Shirazi |
| collection | DOAJ |
| description | In recent years, improved deep learning techniques have been applied to biomedical image processing for the classification and segmentation of different tumors based on magnetic resonance imaging (MRI) and histopathological imaging (H&E) clinical information. Deep Convolutional Neural Networks (DCNNs) architectures include tens to hundreds of processing layers that can extract multiple levels of features in image-based data, which would be otherwise very difficult and time-consuming to be recognized and extracted by experts for classification of tumors into different tumor types, as well as segmentation of tumor images. This article summarizes the latest studies of deep learning techniques applied to three different kinds of brain cancer medical images (histology, magnetic resonance, and computed tomography) and highlights current challenges in the field for the broader applicability of DCNN in personalized brain cancer care by focusing on two main applications of DCNNs: classification and segmentation of brain cancer tumors images. |
| first_indexed | 2024-03-10T14:53:37Z |
| format | Article |
| id | doaj.art-1d7cded849f048dfb52c665a26dd5ee0 |
| institution | Directory Open Access Journal |
| issn | 2075-4426 |
| language | English |
| last_indexed | 2024-03-10T14:53:37Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Personalized Medicine |
| spelling | doaj.art-1d7cded849f048dfb52c665a26dd5ee02023-11-20T20:45:04ZengMDPI AGJournal of Personalized Medicine2075-44262020-11-0110422410.3390/jpm10040224The Application of Deep Convolutional Neural Networks to Brain Cancer Images: A SurveyAmin Zadeh Shirazi0Eric Fornaciari1Mark D. McDonnell2Mahdi Yaghoobi3Yesenia Cevallos4Luis Tello-Oquendo5Deysi Inca6Guillermo A. Gomez7Centre for Cancer Biology, SA Pathology and the University of South of Australia, Adelaide, SA 5000, AustraliaDepartment of Mathematics of Computation, University of California, Los Angeles (UCLA), Los Angeles, CA 90095, USAComputational Learning Systems Laboratory, UniSA STEM, University of South Australia, Mawson Lakes, SA 5095, AustraliaElectrical and Computer Engineering Department, Islamic Azad University, Mashhad Branch, Mashad 917794-8564, IranCollege of Engineering, Universidad Nacional de Chimborazo, Riobamba 060150, EcuadorCollege of Engineering, Universidad Nacional de Chimborazo, Riobamba 060150, EcuadorCollege of Engineering, Universidad Nacional de Chimborazo, Riobamba 060150, EcuadorCentre for Cancer Biology, SA Pathology and the University of South of Australia, Adelaide, SA 5000, AustraliaIn recent years, improved deep learning techniques have been applied to biomedical image processing for the classification and segmentation of different tumors based on magnetic resonance imaging (MRI) and histopathological imaging (H&E) clinical information. Deep Convolutional Neural Networks (DCNNs) architectures include tens to hundreds of processing layers that can extract multiple levels of features in image-based data, which would be otherwise very difficult and time-consuming to be recognized and extracted by experts for classification of tumors into different tumor types, as well as segmentation of tumor images. This article summarizes the latest studies of deep learning techniques applied to three different kinds of brain cancer medical images (histology, magnetic resonance, and computed tomography) and highlights current challenges in the field for the broader applicability of DCNN in personalized brain cancer care by focusing on two main applications of DCNNs: classification and segmentation of brain cancer tumors images.https://www.mdpi.com/2075-4426/10/4/224deep learningDCNNconvolutional neural networksbrain cancerMRIhistology |
| spellingShingle | Amin Zadeh Shirazi Eric Fornaciari Mark D. McDonnell Mahdi Yaghoobi Yesenia Cevallos Luis Tello-Oquendo Deysi Inca Guillermo A. Gomez The Application of Deep Convolutional Neural Networks to Brain Cancer Images: A Survey Journal of Personalized Medicine deep learning DCNN convolutional neural networks brain cancer MRI histology |
| title | The Application of Deep Convolutional Neural Networks to Brain Cancer Images: A Survey |
| title_full | The Application of Deep Convolutional Neural Networks to Brain Cancer Images: A Survey |
| title_fullStr | The Application of Deep Convolutional Neural Networks to Brain Cancer Images: A Survey |
| title_full_unstemmed | The Application of Deep Convolutional Neural Networks to Brain Cancer Images: A Survey |
| title_short | The Application of Deep Convolutional Neural Networks to Brain Cancer Images: A Survey |
| title_sort | application of deep convolutional neural networks to brain cancer images a survey |
| topic | deep learning DCNN convolutional neural networks brain cancer MRI histology |
| url | https://www.mdpi.com/2075-4426/10/4/224 |
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