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Multimodal hybrid convolutional neural network based brain tumor grade classification

An abnormal growth or fatty mass of cells in the brain is called a tumor. They can be either healthy (normal) or become cancerous, depending on the structure of their cells. This can result in increased pressure within the cranium, potentially causing damage to the brain or even death. As a result,...

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Autores principales: Rohini, A., Praveen, Carol, Mathivanan, Sandeep Kumar, Muthukumaran, V., Mallik, Saurav, Alqahtani, Mohammed S., Al-Rasheed, Amal, Soufiene, Ben Othman
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10566188/
https://www.ncbi.nlm.nih.gov/pubmed/37817066
http://dx.doi.org/10.1186/s12859-023-05518-3
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author Rohini, A.
Praveen, Carol
Mathivanan, Sandeep Kumar
Muthukumaran, V.
Mallik, Saurav
Alqahtani, Mohammed S.
Al-Rasheed, Amal
Soufiene, Ben Othman
author_facet Rohini, A.
Praveen, Carol
Mathivanan, Sandeep Kumar
Muthukumaran, V.
Mallik, Saurav
Alqahtani, Mohammed S.
Al-Rasheed, Amal
Soufiene, Ben Othman
author_sort Rohini, A.
collection PubMed
description An abnormal growth or fatty mass of cells in the brain is called a tumor. They can be either healthy (normal) or become cancerous, depending on the structure of their cells. This can result in increased pressure within the cranium, potentially causing damage to the brain or even death. As a result, diagnostic procedures such as computed tomography, magnetic resonance imaging, and positron emission tomography, as well as blood and urine tests, are used to identify brain tumors. However, these methods can be labor-intensive and sometimes yield inaccurate results. Instead of these time-consuming methods, deep learning models are employed because they are less time-consuming, require less expensive equipment, produce more accurate results, and are easy to set up. In this study, we propose a method based on transfer learning, utilizing the pre-trained VGG-19 model. This approach has been enhanced by applying a customized convolutional neural network framework and combining it with pre-processing methods, including normalization and data augmentation. For training and testing, our proposed model used 80% and 20% of the images from the dataset, respectively. Our proposed method achieved remarkable success, with an accuracy rate of 99.43%, a sensitivity of 98.73%, and a specificity of 97.21%. The dataset, sourced from Kaggle for training purposes, consists of 407 images, including 257 depicting brain tumors and 150 without tumors. These models could be utilized to develop clinically useful solutions for identifying brain tumors in CT images based on these outcomes.
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spelling pubmed-105661882023-10-12 Multimodal hybrid convolutional neural network based brain tumor grade classification Rohini, A. Praveen, Carol Mathivanan, Sandeep Kumar Muthukumaran, V. Mallik, Saurav Alqahtani, Mohammed S. Al-Rasheed, Amal Soufiene, Ben Othman BMC Bioinformatics Research An abnormal growth or fatty mass of cells in the brain is called a tumor. They can be either healthy (normal) or become cancerous, depending on the structure of their cells. This can result in increased pressure within the cranium, potentially causing damage to the brain or even death. As a result, diagnostic procedures such as computed tomography, magnetic resonance imaging, and positron emission tomography, as well as blood and urine tests, are used to identify brain tumors. However, these methods can be labor-intensive and sometimes yield inaccurate results. Instead of these time-consuming methods, deep learning models are employed because they are less time-consuming, require less expensive equipment, produce more accurate results, and are easy to set up. In this study, we propose a method based on transfer learning, utilizing the pre-trained VGG-19 model. This approach has been enhanced by applying a customized convolutional neural network framework and combining it with pre-processing methods, including normalization and data augmentation. For training and testing, our proposed model used 80% and 20% of the images from the dataset, respectively. Our proposed method achieved remarkable success, with an accuracy rate of 99.43%, a sensitivity of 98.73%, and a specificity of 97.21%. The dataset, sourced from Kaggle for training purposes, consists of 407 images, including 257 depicting brain tumors and 150 without tumors. These models could be utilized to develop clinically useful solutions for identifying brain tumors in CT images based on these outcomes. BioMed Central 2023-10-10 /pmc/articles/PMC10566188/ /pubmed/37817066 http://dx.doi.org/10.1186/s12859-023-05518-3 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Rohini, A.
Praveen, Carol
Mathivanan, Sandeep Kumar
Muthukumaran, V.
Mallik, Saurav
Alqahtani, Mohammed S.
Al-Rasheed, Amal
Soufiene, Ben Othman
Multimodal hybrid convolutional neural network based brain tumor grade classification
title Multimodal hybrid convolutional neural network based brain tumor grade classification
title_full Multimodal hybrid convolutional neural network based brain tumor grade classification
title_fullStr Multimodal hybrid convolutional neural network based brain tumor grade classification
title_full_unstemmed Multimodal hybrid convolutional neural network based brain tumor grade classification
title_short Multimodal hybrid convolutional neural network based brain tumor grade classification
title_sort multimodal hybrid convolutional neural network based brain tumor grade classification
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10566188/
https://www.ncbi.nlm.nih.gov/pubmed/37817066
http://dx.doi.org/10.1186/s12859-023-05518-3
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