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.

References

[1]

L. N. Wu. 2008. Pattern recognition via PCNN and tsallis entropy. Sensors 8 (2008), 7518–7529.

[2]

L. N. Wu. 2009. Segment-based coding of color images. Science in China Series F-Information Sciences 52 (2009), 914–925.

[3]

F. Ucar and D. Korkmaz. 2020. COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images. Medical Hypotheses 140 (2020), 109761.

[4]

T. Ozturk, M. Talo, E. A. Yildirim, U. B. Baloglu, O. Yildirim, and U. Rajendra Acharya. 2020. Automated detection of COVID-19 cases using deep neural networks with X-ray images. Computers in Biology and Medicine 121 (2020).

[5]

I. D. Apostolopoulos and T. A. Mpesiana. 2020. Covid-19: Automatic detection from X-ray images utilizing transfer learning with convolutional neural networks. Physical and Engineering Sciences in Medicine 43 (2020), 635–640.

[6]

I. D. Apostolopoulos, S. I. Aznaouridis, and M. A. Tzani. 2020. Extracting possibly representative COVID-19 biomarkers from X-ray images with deep learning approach and image data related to pulmonary diseases. Journal of Medical and Biological Engineering 40 (2020), 462–469.

[7]

Fatima M. Salman, Samy S. Abu-Naser, Eman Alajrami, Bassem S. Abu-Nasser, and B. A. M. Ashqar. 2020. COVID-19 detection using artificial intelligence. International Journal of Academic Engineering Research (IJAER) 4 (2020), 18–25.

[8]

A. E. Hassanien, L. N. Mahdy, K. A. Ezzat, H. H. Elmousalami, and H. Aboul Ella. 2020. Automatic X-ray COVID-19 lung image classification system based on multi-level thresholding and support vector machine. medRxiv 2020.

[9]

C. Zheng, X. Deng, Q. Fu, Q. Zhou, J. Feng, and H. Ma. 2020. Deep learning-based detection for COVID-19 from chest CT using Weak Label. medRxiv 2020.

[10]

S. Wang, B. Kang, J. Ma, X. Zeng, M. Xiao, and J. Guo. 2020. A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19). medRxiv 2020.

[11]

Yu-Huan Wu, Shang-Hua Gao, Jie Mei, Jun Xu, Deng-Ping Fan, and Chao-Wei Zhao. 2020. JCS: An explainable COVID-19 diagnosis system by joint classification and segmentation. arXiv:2004.07054 2020.

[12]

Parnian Afshar, Shahin Heidarian, Farnoosh Naderkhani, Anastasia Oikonomou, Konstantinos N. Plataniotis, and A. Mohammadi. 2020. COVID-CAPS: A capsule network-based framework for identification of COVID-19 cases from X-ray images. arXiv:2004.02696 2020.

[13]

Jinyu Zhao, Xuehai He, Xingyi Yang, Yichen Zhang, Shanghang Zhang, and P. Xie. 2020. COVID-CT-Dataset: A CT image dataset about COVID-19. arXiv:2003.13865 2020.

[14]

X. Li, C. Li, and D. Zhu. 2020. COVID-MobileXpert: On-device COVID-19 screening using snapshotsOF chest X-rays. arXiv:2004.03042v2 2020.

[15]

A. Abbas, M. M. Abdelsamea, and M. M. Gaber. 2020. Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network. arXiv:2003.13815v3 2020.

[16]

Mucahid Barstugan, Umut Ozkaya, and S. Ozturk. 2020. Coronavirus (COVID-19) classification using CT images by machine learning methods. arXiv:2003.09424 2020.

[17]

Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, and W. Hubbard. 1989. Backpropagation applied to handwritten zip code recognition. Neural Computation 1 (1989), 541–551.

Digital Library

[18]

Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. 1998. Gradient-based learning applied to document recognition. Proceedings of the IEEE 86 (1998), 2278–2324.

[19]

V. V. Govindaraj. 2019. High performance multiple sclerosis classification by data augmentation and AlexNet transfer learning model. Journal of Medical Imaging and Health Informatics 9 (2019), 2012–2021.

[20]

S. C. Satapathy. 2021. A five-layer deep convolutional neural network with stochastic pooling for chest CT-based COVID-19 diagnosis. Machine Vision and Applications 32, Article ID: 14, 2021.

Digital Library

[21]

A. Krizhevsky, I. Sutskever, and G. Hinton. 2012. ImageNet classification with deep convolutional neural networks. International Conference on Neural Information Processing Systems. 1097–1105.

Digital Library

[22]

K. Simonyan and A. Zisserman. 2015. Very deep convolutional networks for large-scale image recognition. International Conference on Learning Representations 2015.

[23]

K. He, X. Zhang, S. Ren, and J. Sun. 2016. Deep residual learning for image recognition. Presented at The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016.

[24]

M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen. 2018. MobileNetV2: Inverted residuals and linear bottlenecks. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 4510–4520.

[25]

F. N. Iandola, S. Han, M. W. Moskewicz, K. Ashraf, W. J. Dally, and K. Keutzer. 2016. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5 MB model size. arXiv:1602.07360 2016.

[26]

C. Pan. 2018. Abnormal breast identification by nine-layer convolutional neural network with parametric rectified linear unit and rank-based stochastic pooling. Journal of Computational Science 27 (2018), 57–68.

[27]

C. Pan. 2018. Multiple sclerosis identification by convolutional neural network with dropout and parametric ReLU. Journal of Computational Science 28 (2018), 1–10.

[28]

Y. Wang, F. Cao, and Y. Yuan. 2011. A study on effectiveness of extreme learning machine. Neurocomputing 74 (2011), 2483–2490.

[29]

H. Guang-Bin, Z. Qin-Yu, and S. Chee-Kheong. 2006. Extreme learning machine: Theory and applications. Neurocomputing 70 (2006), 489–501.

[30]

W. F. Schmidt, M. A. Kraaijveld, and R. P. W. Duin. 1992. Feedforward neural networks with random weights. Presented at the Proceedings., 11th IAPR International Conference on Pattern Recognition. Vol.II. Conference B: Pattern Recognition Methodology and Systems 1992.

[31]

Y. H. Pao, G. H. Park, and D. J. Sobajic. 1994. Learning and generalization characteristics of random vector functional-link net. Neurocomputing 6 (1994), 163–180.

[32]

M. Haghighat, M. Abdel-Mottaleb, and W. Alhalabi. 2016. Discriminant correlation analysis: Real-time feature level fusion for multimodal biometric recognition. IEEE Transactions on Information Forensics and Security 11 (2016), 1984–1996.

Digital Library

[33]

P. L. Bartlett. 1998. The sample complexity of pattern classification with neural networks: The size of the weights is more important than the size of the network. IEEE Transactions on Information Theory 44 (1998), 525–536.

Digital Library

[34]

S. Ruder. 2017. An overview of gradient descent optimization algorithms. arXiv:1609.04747. 2017.

[35]

A. Mangal, S. Kalia, H. Rajgopal, K. Rangarajan, V. Namboodiri, and S. Banerjee. 2020. CovidAID: COVID-19 detection using chest X-ray. arXiv preprint arXiv:2004.09803. 2020.

[36]

S. H. Wang, V. V. Govindaraj, J. M. Gorriz, X. Zhang, and Y. D. Zhang. 2021. Covid-19 classification by FGCNet with deep feature fusion from graph convolutional network and convolutional neural network. Information Fusion 67 (2021), 208–229.

[37]

Z. Yu, X. Li, H. Sun, J. Wang, T. Zhao, and H. Chen. 2020. Rapid identification of COVID-19 severity in CT scans through classification of deep features. BioMedical Engineering OnLine 19 (2020).

[38]

A. Waheed, M. Goyal, D. Gupta, A. Khanna, F. Al-Turjman, and P. R. Pinheiro. 2020. CovidGAN: Data augmentation using auxiliary classifier GAN for improved Covid-19 detection. IEEE Access 8 (2020), 91916–91923.

[39]

L. Li, L. Qin, Z. Xu, Y. Yin, X. Wang, and B. Kong. 2020. Artificial intelligence distinguishes Covid-19 from community acquired pneumonia on chest CT. Radiology (2020), 200905.

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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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Association for Computing Machinery

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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://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
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