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Type: Journal article
Title: The application of deep convolutional neural networks to brain cancer images: a survey
Author: Shirazi, A.Z.
Fornaciari, E.
McDonnell, M.D.
Yaghoobi, M.
Cevallos, Y.
Tello-Oquendo, L.
Inca, D.
Gomez, G.A.
Citation: Journal of Personalized Medicine, 2020; 10(4):1-27
Publisher: MDPI
Issue Date: 2020
ISSN: 2075-4426
Statement of
Amin Zadeh Shirazi, Eric Fornaciari, Mark D. McDonnell, Mahdi Yaghoobi, Yesenia Cevallos, Luis Tello-Oquendo ... et al.
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.
Keywords: DCNN
brain cancer
convolutional neural networks
deep learning
Rights: © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (
DOI: 10.3390/jpm10040224
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