Preliminary study on patch sizes in vision transformers (ViT) for COVID-19 and diseased lungs classification
Document Type
Conference Item
Publication Date
1-1-2021
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.
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
Divisions
MathematicalSciences
Funders
None
Publication Title
1st National Biomedical Engineering Conference, NBEC 2021
Event Title
1st National Biomedical Engineering Conference (Nbec 2021): Advanced Technology for Modern Healthcare
Event Location
ELECTR Network
Event Dates
9-10 November 2021
Event Type
conference