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Classification of Brain Tumor from Magnetic Resonance Imaging Using Vision Transformers Ensembling
The automated classification of brain tumors plays an important role in supporting radiologists in decision making. Recently, vision transformer (ViT)-based deep neural network architectures have gained attention in the computer vision research domain owing to the tremendous success of transformer m...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9600395/ https://www.ncbi.nlm.nih.gov/pubmed/36290867 http://dx.doi.org/10.3390/curroncol29100590 |
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author | Tummala, Sudhakar Kadry, Seifedine Bukhari, Syed Ahmad Chan Rauf, Hafiz Tayyab |
author_facet | Tummala, Sudhakar Kadry, Seifedine Bukhari, Syed Ahmad Chan Rauf, Hafiz Tayyab |
author_sort | Tummala, Sudhakar |
collection | PubMed |
description | The automated classification of brain tumors plays an important role in supporting radiologists in decision making. Recently, vision transformer (ViT)-based deep neural network architectures have gained attention in the computer vision research domain owing to the tremendous success of transformer models in natural language processing. Hence, in this study, the ability of an ensemble of standard ViT models for the diagnosis of brain tumors from T1-weighted (T1w) magnetic resonance imaging (MRI) is investigated. Pretrained and finetuned ViT models (B/16, B/32, L/16, and L/32) on ImageNet were adopted for the classification task. A brain tumor dataset from figshare, consisting of 3064 T1w contrast-enhanced (CE) MRI slices with meningiomas, gliomas, and pituitary tumors, was used for the cross-validation and testing of the ensemble ViT model’s ability to perform a three-class classification task. The best individual model was L/32, with an overall test accuracy of 98.2% at 384 × 384 resolution. The ensemble of all four ViT models demonstrated an overall testing accuracy of 98.7% at the same resolution, outperforming individual model’s ability at both resolutions and their ensembling at 224 × 224 resolution. In conclusion, an ensemble of ViT models could be deployed for the computer-aided diagnosis of brain tumors based on T1w CE MRI, leading to radiologist relief. |
format | Online Article Text |
id | pubmed-9600395 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-96003952022-10-27 Classification of Brain Tumor from Magnetic Resonance Imaging Using Vision Transformers Ensembling Tummala, Sudhakar Kadry, Seifedine Bukhari, Syed Ahmad Chan Rauf, Hafiz Tayyab Curr Oncol Article The automated classification of brain tumors plays an important role in supporting radiologists in decision making. Recently, vision transformer (ViT)-based deep neural network architectures have gained attention in the computer vision research domain owing to the tremendous success of transformer models in natural language processing. Hence, in this study, the ability of an ensemble of standard ViT models for the diagnosis of brain tumors from T1-weighted (T1w) magnetic resonance imaging (MRI) is investigated. Pretrained and finetuned ViT models (B/16, B/32, L/16, and L/32) on ImageNet were adopted for the classification task. A brain tumor dataset from figshare, consisting of 3064 T1w contrast-enhanced (CE) MRI slices with meningiomas, gliomas, and pituitary tumors, was used for the cross-validation and testing of the ensemble ViT model’s ability to perform a three-class classification task. The best individual model was L/32, with an overall test accuracy of 98.2% at 384 × 384 resolution. The ensemble of all four ViT models demonstrated an overall testing accuracy of 98.7% at the same resolution, outperforming individual model’s ability at both resolutions and their ensembling at 224 × 224 resolution. In conclusion, an ensemble of ViT models could be deployed for the computer-aided diagnosis of brain tumors based on T1w CE MRI, leading to radiologist relief. MDPI 2022-10-07 /pmc/articles/PMC9600395/ /pubmed/36290867 http://dx.doi.org/10.3390/curroncol29100590 Text en © 2022 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 Tummala, Sudhakar Kadry, Seifedine Bukhari, Syed Ahmad Chan Rauf, Hafiz Tayyab Classification of Brain Tumor from Magnetic Resonance Imaging Using Vision Transformers Ensembling |
title | Classification of Brain Tumor from Magnetic Resonance Imaging Using Vision Transformers Ensembling |
title_full | Classification of Brain Tumor from Magnetic Resonance Imaging Using Vision Transformers Ensembling |
title_fullStr | Classification of Brain Tumor from Magnetic Resonance Imaging Using Vision Transformers Ensembling |
title_full_unstemmed | Classification of Brain Tumor from Magnetic Resonance Imaging Using Vision Transformers Ensembling |
title_short | Classification of Brain Tumor from Magnetic Resonance Imaging Using Vision Transformers Ensembling |
title_sort | classification of brain tumor from magnetic resonance imaging using vision transformers ensembling |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9600395/ https://www.ncbi.nlm.nih.gov/pubmed/36290867 http://dx.doi.org/10.3390/curroncol29100590 |
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