The Application of Deep Convolutional Neural Networks to Brain Cancer Images: A Survey

Zadeh Shirazi, Amin; Fornaciari, Eric; McDonnell, Mark D.; Yaghoobi, Mahdi; Cevallos, Yesenia; Tello-Oquendo, Luis; Inca, Deysi; Gomez, Guillermo A.

doi:10.3390/jpm10040224

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The Application of Deep Convolutional Neural Networks to Brain Cancer Images: A Survey

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Centre for Cancer Biology, SA Pathology and the University of South of Australia, Adelaide, SA 5000, Australia

Computational Learning Systems Laboratory, UniSA STEM, University of South Australia, Mawson Lakes, SA 5095, Australia

Department of Mathematics of Computation, University of California, Los Angeles (UCLA), Los Angeles, CA 90095, USA

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Electrical and Computer Engineering Department, Islamic Azad University, Mashhad Branch, Mashad 917794-8564, Iran

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College of Engineering, Universidad Nacional de Chimborazo, Riobamba 060150, Ecuador

Author to whom correspondence should be addressed.

J. Pers. Med. 2020, 10(4), 224; https://doi.org/10.3390/jpm10040224

Received: 29 September 2020 / Revised: 10 November 2020 / Accepted: 10 November 2020 / Published: 12 November 2020

(This article belongs to the Special Issue Biomedical Imaging and Cancers)

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Abstract

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. View Full-Text

Keywords: deep learning; DCNN; convolutional neural networks; brain cancer; MRI; histology; classification; segmentation

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MDPI and ACS Style

Zadeh Shirazi, A.; Fornaciari, E.; McDonnell, M.D.; Yaghoobi, M.; Cevallos, Y.; Tello-Oquendo, L.; Inca, D.; Gomez, G.A. The Application of Deep Convolutional Neural Networks to Brain Cancer Images: A Survey. J. Pers. Med. 2020, 10, 224. https://doi.org/10.3390/jpm10040224

AMA Style

Zadeh Shirazi A, Fornaciari E, McDonnell MD, Yaghoobi M, Cevallos Y, Tello-Oquendo L, Inca D, Gomez GA. The Application of Deep Convolutional Neural Networks to Brain Cancer Images: A Survey. Journal of Personalized Medicine. 2020; 10(4):224. https://doi.org/10.3390/jpm10040224

Chicago/Turabian Style

Zadeh Shirazi, Amin, Eric Fornaciari, Mark D. McDonnell, Mahdi Yaghoobi, Yesenia Cevallos, Luis Tello-Oquendo, Deysi Inca, and Guillermo A. Gomez. 2020. "The Application of Deep Convolutional Neural Networks to Brain Cancer Images: A Survey" Journal of Personalized Medicine 10, no. 4: 224. https://doi.org/10.3390/jpm10040224

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