Abstract
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 computeraided brain tumor classification models are severely limited to smaller datasets of only 4 MRI contrasts: T2, T2/FLAIR, and T1
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, T1 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.
Index Terms—Brain Tumor, MRI, T1-weighted, T2-weighted, Swin Transformers, MaxViT-Tiny
tumors. Despite the advances, most of the existing computeraided brain tumor classification models are severely limited to smaller datasets of only 4 MRI contrasts: T2, T2/FLAIR, and T1
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, T1 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.
Index Terms—Brain Tumor, MRI, T1-weighted, T2-weighted, Swin Transformers, MaxViT-Tiny
| Original language | English |
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| Title of host publication | Proceedings, 2023 International Conference on Digital Image Computing |
| Subtitle of host publication | Techniques and Applications (DICTA) |
| Publisher | IEEE |
| Pages | 289-295 |
| Number of pages | 7 |
| ISBN (Electronic) | 9798350382204 |
| ISBN (Print) | 9798350382211 (Print on demand) |
| DOIs | |
| Publication status | Published - 2023 |
| Event | The International Conference on Digital Image Computing: Techniques and Applications: DICTA 2023 - Sails Port Macquarie, Port Macquarie, Australia Duration: 28 Nov 2023 → 01 Dec 2023 https://www.dictaconference.org/ https://www.dictaconference.org/?page_id=2623 (Conference program) |
Conference
| Conference | The International Conference on Digital Image Computing: Techniques and Applications |
|---|---|
| Country/Territory | Australia |
| City | Port Macquarie |
| Period | 28/11/23 → 01/12/23 |
| Other | Digital Image Computing: Techniques and Applications (DICTA) is the main Australian Conference on computer vision, image processing, pattern recognition, and related areas. DICTA was established in 1991 as the premier conference of the Australian Pattern Recognition Society (APRS). DICTA provides a forum for researchers, engineers, and practitioners to present their latest findings and innovations in these areas, as well as to exchange ideas and discuss emerging trends and challenges in the field. The conference covers a wide range of topics, including image and video processing, machine learning, pattern recognition, and computer graphics, among others. |
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