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Brain Tumor Segmentation Based on Deep Learning’s Feature Representation

Brain tumor is considered as one of the most serious causes of death in the world. Thus, it is very important to detect it as early as possible. In order to predict and segment the tumor, many approaches have been proposed. However, they suffer from different problems such as the necessity of the in...

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Autores principales: Aboussaleh, Ilyasse, Riffi, Jamal, Mahraz, Adnane Mohamed, Tairi, Hamid
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8703314/
https://www.ncbi.nlm.nih.gov/pubmed/34940736
http://dx.doi.org/10.3390/jimaging7120269
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author Aboussaleh, Ilyasse
Riffi, Jamal
Mahraz, Adnane Mohamed
Tairi, Hamid
author_facet Aboussaleh, Ilyasse
Riffi, Jamal
Mahraz, Adnane Mohamed
Tairi, Hamid
author_sort Aboussaleh, Ilyasse
collection PubMed
description Brain tumor is considered as one of the most serious causes of death in the world. Thus, it is very important to detect it as early as possible. In order to predict and segment the tumor, many approaches have been proposed. However, they suffer from different problems such as the necessity of the intervention of a specialist, the long required run-time and the choice of the appropriate feature extractor. To address these issues, we proposed an approach based on convolution neural network architecture aiming at predicting and segmenting simultaneously a cerebral tumor. The proposal was divided into two phases. Firstly, aiming at avoiding the use of the labeled image that implies a subject intervention of the specialist, we used a simple binary annotation that reflects the existence of the tumor or not. Secondly, the prepared image data were fed into our deep learning model in which the final classification was obtained; if the classification indicated the existence of the tumor, the brain tumor was segmented based on the feature representations generated by the convolutional neural network architectures. The proposed method was trained on the BraTS 2017 dataset with different types of gliomas. The achieved results show the performance of the proposed approach in terms of accuracy, precision, recall and Dice similarity coefficient. Our model showed an accuracy of 91% in tumor classification and a Dice similarity coefficient of 82.35% in tumor segmentation.
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spelling pubmed-87033142021-12-25 Brain Tumor Segmentation Based on Deep Learning’s Feature Representation Aboussaleh, Ilyasse Riffi, Jamal Mahraz, Adnane Mohamed Tairi, Hamid J Imaging Article Brain tumor is considered as one of the most serious causes of death in the world. Thus, it is very important to detect it as early as possible. In order to predict and segment the tumor, many approaches have been proposed. However, they suffer from different problems such as the necessity of the intervention of a specialist, the long required run-time and the choice of the appropriate feature extractor. To address these issues, we proposed an approach based on convolution neural network architecture aiming at predicting and segmenting simultaneously a cerebral tumor. The proposal was divided into two phases. Firstly, aiming at avoiding the use of the labeled image that implies a subject intervention of the specialist, we used a simple binary annotation that reflects the existence of the tumor or not. Secondly, the prepared image data were fed into our deep learning model in which the final classification was obtained; if the classification indicated the existence of the tumor, the brain tumor was segmented based on the feature representations generated by the convolutional neural network architectures. The proposed method was trained on the BraTS 2017 dataset with different types of gliomas. The achieved results show the performance of the proposed approach in terms of accuracy, precision, recall and Dice similarity coefficient. Our model showed an accuracy of 91% in tumor classification and a Dice similarity coefficient of 82.35% in tumor segmentation. MDPI 2021-12-08 /pmc/articles/PMC8703314/ /pubmed/34940736 http://dx.doi.org/10.3390/jimaging7120269 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Aboussaleh, Ilyasse
Riffi, Jamal
Mahraz, Adnane Mohamed
Tairi, Hamid
Brain Tumor Segmentation Based on Deep Learning’s Feature Representation
title Brain Tumor Segmentation Based on Deep Learning’s Feature Representation
title_full Brain Tumor Segmentation Based on Deep Learning’s Feature Representation
title_fullStr Brain Tumor Segmentation Based on Deep Learning’s Feature Representation
title_full_unstemmed Brain Tumor Segmentation Based on Deep Learning’s Feature Representation
title_short Brain Tumor Segmentation Based on Deep Learning’s Feature Representation
title_sort brain tumor segmentation based on deep learning’s feature representation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8703314/
https://www.ncbi.nlm.nih.gov/pubmed/34940736
http://dx.doi.org/10.3390/jimaging7120269
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