Than, J.C.M. and Thon, P.L. and Rijal, O.M. and Kassim, R.M. and Yunus, A. and Noor, N.M. and Then, P. (2021) Preliminary study on patch sizes in vision transformers (ViT) for COVID-19 and diseased lungs classification. In: 1st National Biomedical Engineering Conference (Nbec 2021): Advanced Technology for Modern Healthcare, 9-10 November 2021, ELECTR Network.
Full text not available from this repository.Abstract
COVID-19 and lung diseases have been the major focus of research currently due to the pandemic's reach and effect. Deep Learning (DL) is playing a large role today in various fields from disease classification to drug response identification. The conventional DL method used for images is the Convolutional Neural Network (CNN). A potential method that will replace the usage of CNNs is Transformer specifically Vision Transformers (ViT). This study is a preliminary exploration to determine the performance of using ViT on diseased lungs, COVID-19 infected lungs, and normal lungs. This study was performed on two datasets. The first dataset was a publicly accessible dataset from Iran that has a large cohort of patients. The second dataset was a Malaysian dataset. These images were utilized to verify the usage of ViT and its effectiveness. Images were segregated into several sized patches (16x16, 32x32, 64x64, 128x128, 256x256) pixels. To determine the performance of ViT method, performance metrics of accuracy, sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV) and F1-score. From the results of this study, ViT is a promising method with a peak accuracy of 95.36. © 2021 IEEE.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Funders: | None |
| Uncontrolled Keywords: | Convolutional neural networks; Deep learning; Large dataset, Convolutional neural network; COVID-19; Deep learning; Disease classification; Drug response; Focus of researches; Learning methods; Patch size; Performance; Transformer, Biological organs |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > T Technology (General) |
| Divisions: | Faculty of Science > Institute of Mathematical Sciences |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 27 Nov 2024 00:54 |
| Last Modified: | 27 Nov 2024 00:54 |
| URI: | http://eprints.um.edu.my/id/eprint/36150 |
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