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...
| Publicado en: | Current Oncology Vol. 29; no. 10; pp. 7498 - 7512 |
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| Autores principales: | , , , |
| Formato: | Journal Article |
| Publicado: |
MDPI
Oct2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=159900856&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 159900856 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 11980052 5EKK jtl: Current Oncology issn: 11980052 maglogo: N pubinfo: dt: Oct2022 vid: 29 iid: 10 pid: 97109 pub: MDPI artinfo: ui: 159900856 10.3390/curroncol29100590 159900856 ppf: 7498 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Classification of Brain Tumor from Magnetic Resonance Imaging Using Vision Transformers Ensembling. aug: au: Tummala, Sudhakar Kadry, Seifedine Bukhari, Syed Ahmad Chan Rauf, Hafiz Tayyab affil: Department of Electronics and Communication Engineering, School of Engineering and Sciences, SRM University—AP, Amaravati 522503, India sug: ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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