research-article

An Explainable Framework for Diagnosis of COVID-19 Pneumonia via Transfer Learning and Discriminant Correlation Analysis

Authors:

Shui-Hua WangAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Volume 17, Issue 3s

Article No.: 103, Pages 1 - 16

https://doi.org/10.1145/3449785

Published: 26 October 2021 Publication History

Abstract

The new coronavirus COVID-19 has been spreading all over the world in the last six months, and the death toll is still rising. The accurate diagnosis of COVID-19 is an emergent task as to stop the spreading of the virus. In this paper, we proposed to leverage image feature fusion for the diagnosis of COVID-19 in lung window computed tomography (CT). Initially, ResNet-18 and ResNet-50 were selected as the backbone deep networks to generate corresponding image representations from the CT images. Second, the representative information extracted from the two networks was fused by discriminant correlation analysis to obtain refined image features. Third, three randomized neural networks (RNNs): extreme learning machine, Schmidt neural network and random vector functional-link net, were trained using the refined features, and the predictions of the three RNNs were ensembled to get a more robust classification performance. Experiment results based on five-fold cross validation suggested that our method outperformed state-of-the-art algorithms in the diagnosis of COVID-19.

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Index Terms

An Explainable Framework for Diagnosis of COVID-19 Pneumonia via Transfer Learning and Discriminant Correlation Analysis
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision

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cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 17, Issue 3s

October 2021

324 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/3492435

Editor:
Alberto Del Bimbo
University of Firenze, Italy

Issue’s Table of Contents

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Publication History

Published: 26 October 2021

Accepted: 01 February 2021

Revised: 01 January 2021

Received: 01 December 2020

Published in TOMM Volume 17, Issue 3s

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Royal Society International Exchanges Cost Share Award, UK
Medical Research Council Confidence in Concept Award, UK
Hope Foundation for Cancer Research, UK
Guangxi Key Laboratory of Trusted Software
Fundamental Research Funds for the Central Universities
Laboratory of Child Development and Learning Science (Southeast University)
Ministry of Education

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https://dl.acm.org/doi/10.1145/3633334
Abirami MKatneni RReddy K S(2024)Hybrid CNN Model for Pulmonary Disease Detection2024 2nd International Conference on Networking and Communications (ICNWC)10.1109/ICNWC60771.2024.10537498(1-8)Online publication date: 2-Apr-2024
https://doi.org/10.1109/ICNWC60771.2024.10537498
Shoeibi AKhodatars MJafari MGhassemi NSadeghi DMoridian PKhadem AAlizadehsani RHussain SZare ASani ZKhozeimeh FNahavandi SAcharya UGorriz J(2024)Automated detection and forecasting of COVID-19 using deep learning techniquesNeurocomputing10.1016/j.neucom.2024.127317577:COnline publication date: 25-Jun-2024
https://dl.acm.org/doi/10.1016/j.neucom.2024.127317
Husain AVishvakarma V(2023)RES-KELM fusion model based on non-iterative deterministic learning classifier for classification of Covid19 chest X-ray imagesJournal of Intelligent Systems10.1515/jisys-2022-023532:1Online publication date: 2-Jun-2023
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Yao XZhu ZKang CWang SGorriz JZhang Y(2023)AdaD-FNN for Chest CT-Based COVID-19 DiagnosisIEEE Transactions on Emerging Topics in Computational Intelligence10.1109/TETCI.2022.31748687:1(5-14)Online publication date: Feb-2023
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