Improving Non-Invasive Brain Tumor Categorization using Transformers on MRI Data

Nawer, N; Khan, MSI; Reza, MT; Parvez, MZ; Barua, PD; Chakraborty, S

Improving Non-Invasive Brain Tumor Categorization using Transformers on MRI Data

Nawer, N Khan, MSI Reza, MT Parvez, MZ Barua, PD Chakraborty, S

Permalink

Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2023 International Conference on Digital Image Computing: Techniques and Applications (DICTA), 2024, 00, pp. 289-295
Issue Date:: 2024-01-29

Closed Access

	Filename	Description	Size
	Improving_Non-Invasive_Brain_Tumor_Categorization_using_Transformers_on_MRI_Data.pdf	Published version	1.02 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Nawer, N
dc.contributor.author	Khan, MSI
dc.contributor.author	Reza, MT
dc.contributor.author	Parvez, MZ
dc.contributor.author	Barua, PD
dc.contributor.author	Chakraborty, S
dc.date	2023-11-28
dc.date.accessioned	2025-03-11T03:21:08Z
dc.date.available	2025-03-11T03:21:08Z
dc.date.issued	2024-01-29
dc.identifier.citation	2023 International Conference on Digital Image Computing: Techniques and Applications (DICTA), 2024, 00, pp. 289-295
dc.identifier.isbn	979-8-3503-8221-1
dc.identifier.uri	http://hdl.handle.net/10453/185659
dc.description.abstract	Recent years have seen a surge in the number of studies utilizing Artificial Intelligence AI on Magnetic Resonance Imaging MRI to analyze and categorize brain tumors Despite the advances most of the existing computer aided brain tumor classification models are severely limited to smaller datasets of only 4 MRI contrasts T2 T2 FLAIR and Tl pre and post contrast which leads to unsatisfactory performance since the imaging protocols significantly depend on magnetic field strength and acquisition parameters As a result this research aims to address the issue by incorporating the most up to date Glioma MRI dataset UCSF PDGM that includes standardized 3 T three dimensional preoperative MRI protocol diffusion MRI and perfusion MRI In order to acquire a better computational efficiency while extracting image features both locally and globally we have presented two Transformer based approach Swin Transformer and MaxViT Tiny to categorize three types of tumors Astrocytoma Glioblastoma and Oligodendroglioma Considering Tl and T2 weighted MR images are more eligible to classify brain tumors we have trained the two models on these imaging protocols After training and evaluating both the models on performance metrics we have found out that MaxViTTiny slightly outperforms Swin Transformer in classifying brain tumors with an accuracy of 94 84 on T1 dataset and 98 on T2 dataset whereas Swin Transformer achieved 91 05 and 96 97 respectively
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2023 International Conference on Digital Image Computing: Techniques and Applications (DICTA)
dc.relation.ispartof	International Conference on Digital Image Computing: Techniques and Applications
dc.relation.isbasedon	10.1109/dicta60407.2023.00047
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Improving Non-Invasive Brain Tumor Categorization using Transformers on MRI Data
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Port Macquarie, Australia
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2025-03-11T03:21:05Z
pubs.finish-date	2024-12-01
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2023-11-28
pubs.volume	00
dc.location	Piscataway, USA

Abstract:

Recent years have seen a surge in the number of studies utilizing Artificial Intelligence AI on Magnetic Resonance Imaging MRI to analyze and categorize brain tumors Despite the advances most of the existing computer aided brain tumor classification models are severely limited to smaller datasets of only 4 MRI contrasts T2 T2 FLAIR and Tl pre and post contrast which leads to unsatisfactory performance since the imaging protocols significantly depend on magnetic field strength and acquisition parameters As a result this research aims to address the issue by incorporating the most up to date Glioma MRI dataset UCSF PDGM that includes standardized 3 T three dimensional preoperative MRI protocol diffusion MRI and perfusion MRI In order to acquire a better computational efficiency while extracting image features both locally and globally we have presented two Transformer based approach Swin Transformer and MaxViT Tiny to categorize three types of tumors Astrocytoma Glioblastoma and Oligodendroglioma Considering Tl and T2 weighted MR images are more eligible to classify brain tumors we have trained the two models on these imaging protocols After training and evaluating both the models on performance metrics we have found out that MaxViTTiny slightly outperforms Swin Transformer in classifying brain tumors with an accuracy of 94 84 on T1 dataset and 98 on T2 dataset whereas Swin Transformer achieved 91 05 and 96 97 respectively

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/185659