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Brain tumor detection and segmentation: Interactive framework with a visual interface and feedback facility for dynamically improved accuracy and trust

Brain cancers caused by malignant brain tumors are one of the most fatal cancer types with a low survival rate mostly due to the difficulties in early detection. Medical professionals therefore use various invasive and non-invasive methods for detecting and treating brain tumors at the earlier stage...

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Autores principales: Sailunaz, Kashfia, Bestepe, Deniz, Alhajj, Sleiman, Özyer, Tansel, Rokne, Jon, Alhajj, Reda
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10109523/
https://www.ncbi.nlm.nih.gov/pubmed/37068084
http://dx.doi.org/10.1371/journal.pone.0284418
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author Sailunaz, Kashfia
Bestepe, Deniz
Alhajj, Sleiman
Özyer, Tansel
Rokne, Jon
Alhajj, Reda
author_facet Sailunaz, Kashfia
Bestepe, Deniz
Alhajj, Sleiman
Özyer, Tansel
Rokne, Jon
Alhajj, Reda
author_sort Sailunaz, Kashfia
collection PubMed
description Brain cancers caused by malignant brain tumors are one of the most fatal cancer types with a low survival rate mostly due to the difficulties in early detection. Medical professionals therefore use various invasive and non-invasive methods for detecting and treating brain tumors at the earlier stages thus enabling early treatment. The main non-invasive methods for brain tumor diagnosis and assessment are brain imaging like computed tomography (CT), positron emission tomography (PET) and magnetic resonance imaging (MRI) scans. In this paper, the focus is on detection and segmentation of brain tumors from 2D and 3D brain MRIs. For this purpose, a complete automated system with a web application user interface is described which detects and segments brain tumors with more than 90% accuracy and Dice scores. The user can upload brain MRIs or can access brain images from hospital databases to check presence or absence of brain tumor, to check the existence of brain tumor from brain MRI features and to extract the tumor region precisely from the brain MRI using deep neural networks like CNN, U-Net and U-Net++. The web application also provides an option for entering feedbacks on the results of the detection and segmentation to allow healthcare professionals to add more precise information on the results that can be used to train the model for better future predictions and segmentations.
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spelling pubmed-101095232023-04-18 Brain tumor detection and segmentation: Interactive framework with a visual interface and feedback facility for dynamically improved accuracy and trust Sailunaz, Kashfia Bestepe, Deniz Alhajj, Sleiman Özyer, Tansel Rokne, Jon Alhajj, Reda PLoS One Research Article Brain cancers caused by malignant brain tumors are one of the most fatal cancer types with a low survival rate mostly due to the difficulties in early detection. Medical professionals therefore use various invasive and non-invasive methods for detecting and treating brain tumors at the earlier stages thus enabling early treatment. The main non-invasive methods for brain tumor diagnosis and assessment are brain imaging like computed tomography (CT), positron emission tomography (PET) and magnetic resonance imaging (MRI) scans. In this paper, the focus is on detection and segmentation of brain tumors from 2D and 3D brain MRIs. For this purpose, a complete automated system with a web application user interface is described which detects and segments brain tumors with more than 90% accuracy and Dice scores. The user can upload brain MRIs or can access brain images from hospital databases to check presence or absence of brain tumor, to check the existence of brain tumor from brain MRI features and to extract the tumor region precisely from the brain MRI using deep neural networks like CNN, U-Net and U-Net++. The web application also provides an option for entering feedbacks on the results of the detection and segmentation to allow healthcare professionals to add more precise information on the results that can be used to train the model for better future predictions and segmentations. Public Library of Science 2023-04-17 /pmc/articles/PMC10109523/ /pubmed/37068084 http://dx.doi.org/10.1371/journal.pone.0284418 Text en © 2023 Sailunaz et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Sailunaz, Kashfia
Bestepe, Deniz
Alhajj, Sleiman
Özyer, Tansel
Rokne, Jon
Alhajj, Reda
Brain tumor detection and segmentation: Interactive framework with a visual interface and feedback facility for dynamically improved accuracy and trust
title Brain tumor detection and segmentation: Interactive framework with a visual interface and feedback facility for dynamically improved accuracy and trust
title_full Brain tumor detection and segmentation: Interactive framework with a visual interface and feedback facility for dynamically improved accuracy and trust
title_fullStr Brain tumor detection and segmentation: Interactive framework with a visual interface and feedback facility for dynamically improved accuracy and trust
title_full_unstemmed Brain tumor detection and segmentation: Interactive framework with a visual interface and feedback facility for dynamically improved accuracy and trust
title_short Brain tumor detection and segmentation: Interactive framework with a visual interface and feedback facility for dynamically improved accuracy and trust
title_sort brain tumor detection and segmentation: interactive framework with a visual interface and feedback facility for dynamically improved accuracy and trust
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10109523/
https://www.ncbi.nlm.nih.gov/pubmed/37068084
http://dx.doi.org/10.1371/journal.pone.0284418
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