Xue, S. and Abhayaratne, C. orcid.org/0000-0002-2799-7395 (2021) Covid-19 diagnostic using 3D deep transfer learning for classification of volumetric computerised tomography chest scans. In: ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). ICASSP 2021 - IEEE International Conference on Acoustics, Speech and Signal Processing, 06-11 Jun 2021, Toronto, Ontario, Canada. Institute of Electrical and Electronics Engineers , pp. 8573-8577. ISBN 9781728176062
Abstract
Deep learning-based algorithms provide an efficient and reliable diagnosis for medical imaging. This paper proposes COVID-19 diagnosis based on analysis of Computerised tomography (CT) chest scans. In recent years, deep learning-based analysis of CT chest scans has demonstrated competitive sensitivity for pneumonia prognosis. This paper presents our submission for the 2021 ICASSP Signal Processing Grand Challenge (SPGC). We exploit a 3D Network-based transfer learning approach to classify volumetric CT scans with a novel pre-processing method to render the volume with salient features. This work uses the pre-trained 3D ResNet50 as the backbone network. The 3D network is trained on a dataset consisting of 3 classes: Community Acquired Pneumonia (CAP), COVID-19 and Normal patient. The final testing results have shown an overall accuracy of 85.56% with the COVID-19 sensitivity attaining 82.86%.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | COVID-19; CT scans; Deep Learning; 3D CNN; Transfer Learning; Fully-Automated Classification |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Electronic and Electrical Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 11 Feb 2022 07:30 |
Last Modified: | 13 May 2022 00:38 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers |
Refereed: | Yes |
Identification Number: | 10.1109/icassp39728.2021.9414947 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:183495 |