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A computationally-inexpensive strategy in CT image data augmentation for robust deep learning classification in the early stages of an outbreak
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2307_A computationally-inexpensive strategy in CT image data augmentation for robust deep learning classification in the early stages of an outbreak.pdf | Published version | 1.1 MB | Adobe PDF | View/Open |
Title: | A computationally-inexpensive strategy in CT image data augmentation for robust deep learning classification in the early stages of an outbreak |
Authors: | Hou, Y Navarro-Cia, M |
Item Type: | Journal Article |
Abstract: | Coronavirus disease 2019 (COVID-19) has spread globally for over three years, and chest computed tomography (CT) has been used to diagnose COVID-19 and identify lung damage in COVID-19 patients. Given its widespread, CT will remain a common diagnostic tool in future pandemics, but its effectiveness at the beginning of any pandemic will depend strongly on the ability to classify CT scans quickly and correctly when only limited resources are available, as it will happen inevitably again in future pandemics. Here, we resort into the transfer learning procedure and limited hyperparameters to use as few computing resources as possible for COVID-19 CT images classification. Advanced Normalisation Tools (ANTs) are used to synthesise images as augmented/independent data and trained on EfficientNet to investigate the effect of synthetic images. On the COVID-CT dataset, classification accuracy increases from 91.15% to 95.50% and Area Under the Receiver Operating Characteristic (AUC) from 96.40% to 98.54%. We also customise a small dataset to simulate data collected in the early stages of the outbreak and report an improvement in accuracy from 85.95% to 94.32% and AUC from 93.21% to 98.61%. This study provides a feasible Low-Threshold, Easy-To-Deploy and Ready-To-Use solution with a relatively low computational cost for medical image classification at an early stage of an outbreak in which scarce data are available and traditional data augmentation may fail. Hence, it would be most suitable for low-resource settings. |
Issue Date: | 1-Sep-2023 |
Date of Acceptance: | 6-Jul-2023 |
URI: | http://hdl.handle.net/10044/1/107700 |
DOI: | 10.1088/2057-1976/ace4cf |
ISSN: | 2057-1976 |
Publisher: | IOP Publishing |
Journal / Book Title: | Biomedical Physics & Engineering Express |
Volume: | 9 |
Issue: | 5 |
Copyright Statement: | © 2023 The Author(s). Published by IOP Publishing Ltd. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. |
Publication Status: | Published |
Article Number: | ARTN 055003 |
Online Publication Date: | 2023-07-18 |
Appears in Collections: | Physics Experimental Solid State Faculty of Natural Sciences |
This item is licensed under a Creative Commons License