research-article

Synergic Deep Learning for Smart Health Diagnosis of COVID-19 for Connected Living and Smart Cities

Authors:

Eswaran Perumal,

Mohamed Elhoseny,

Ahmed A. Abd El-LatifAuthors Info & Claims

ACM Transactions on Internet Technology (TOIT), Volume 22, Issue 3

Article No.: 61, Pages 1 - 14

https://doi.org/10.1145/3453168

Published: 29 November 2021 Publication History

Abstract

COVID-19 pandemic has led to a significant loss of global deaths, economical status, and so on. To prevent and control COVID-19, a range of smart, complex, spatially heterogeneous, control solutions, and strategies have been conducted. Earlier classification of 2019 novel coronavirus disease (COVID-19) is needed to cure and control the disease. It results in a requirement of secondary diagnosis models, since no precise automated toolkits exist. The latest finding attained using radiological imaging techniques highlighted that the images hold noticeable details regarding the COVID-19 virus. The application of recent artificial intelligence (AI) and deep learning (DL) approaches integrated to radiological images finds useful to accurately detect the disease. This article introduces a new synergic deep learning (SDL)-based smart health diagnosis of COVID-19 using Chest X-Ray Images. The SDL makes use of dual deep convolutional neural networks (DCNNs) and involves a mutual learning process from one another. Particularly, the representation of images learned by both DCNNs is provided as the input of a synergic network, which has a fully connected structure and predicts whether the pair of input images come under the identical class. Besides, the proposed SDL model involves a fuzzy bilateral filtering (FBF) model to pre-process the input image. The integration of FBL and SDL resulted in the effective classification of COVID-19. To investigate the classifier outcome of the SDL model, a detailed set of simulations takes place and ensures the effective performance of the FBF-SDL model over the compared methods.

References

[1]

Yassine Abdulsalam and M. Shamim Hossain. 2020. COVID-19 networking demand: An auction-based mechanism for automated selection of edge computing services. IEEE Trans. Netw. Sci. Eng. (2020).

[2]

Eman M. Abou-Nassar, Abdullah M. Iliyasu, Passent M. El-Kafrawy, Oh-Young Song, Ali Kashif Bashir, and Ahmed A. Abd El-Latif. 2020. DITrust chain: Towards blockchain-based trust models for sustainable healthcare IoT systems. IEEE Access 8 (2020), 111223–111238.

[3]

Ahmed Alghamdi, Mohamed Hammad, Hassan Ugail, Asmaa Abdel-Raheem, Khan Muhammad, Hany S. Khalifa, and Ahmed A. Abd El-Latif. 2020. Detection of myocardial infarction based on novel deep transfer learning methods for urban healthcare in smart cities. Multim. Tools Applic. (2020), 1–22.

[4]

Ahmed S. Alghamdi, Kemal Polat, Abdullah Alghoson, Abdulrahman A. Alshdadi, and Ahmed A. Abd El-Latif. 2020. Gaussian process regression (GPR) based non-invasive continuous blood pressure prediction method from cuff oscillometric signals. Appl. Acoust. 164 (2020), 107256.

[5]

Ahmed S. Alghamdi, Kemal Polat, Abdullah Alghoson, Abdulrahman A. Alshdadi, and Ahmed A. Abd El-Latif. 2020. A novel blood pressure estimation method based on the classification of oscillometric waveforms using machine-learning methods. Appl. Acoust. 164 (2020), 107279.

[6]

Mohammad A. Alsmirat, Fatimah Al-Alem, Mahmoud Al-Ayyoub, Yaser Jararweh, and Brij Gupta. 2019. Impact of digital fingerprint image quality on the fingerprint recognition accuracy. Multim. Tools Applic. 78, 3 (2019), 3649–3688.

Digital Library

[7]

Ioannis D. Apostolopoulos and Tzani A. Mpesiana. 2020. Covid-19: Automatic detection from x-ray images utilizing transfer learning with convolutional neural networks. Phys. Eng. Sci. Med. (2020), 1.

[8]

Adam Bernheim, Xueyan Mei, Mingqian Huang, Yang Yang, Zahi A. Fayad, Ning Zhang, Kaiyue Diao, Bin Lin, Xiqi Zhu, Kunwei Li, Shaolin Li, Hong Shan, Adam Jacobi, and Michael Chung. 2020. Chest CT findings in coronavirus disease-19 (COVID-19): Relationship to duration of infection. Radiology 295, 3 (June 2020), 200463. DOI:https://doi.org/10.1148/radiol.2020200463

[9]

Federico Caobelli. 2020. Artificial intelligence in medical imaging: Game over for radiologists?Eur. J. Radiol. 126 (May 2020), 108940. DOI:https://doi.org/10.1016/j.ejrad.2020.108940

[10]

Jasper Fuk-Woo Chan, Shuofeng Yuan, Kin-Hang Kok, Kelvin Kai-Wang To, Hin Chu, Jin Yang, Fanfan Xing, Jieling Liu, Cyril Chik-Yan Yip, Rosana Wing-Shan Poon, Hoi-Wah Tsoi, Simon Kam-Fai Lo, Kwok-Hung Chan, Vincent Kwok-Man Poon, Wan-Mui Chan, Jonathan Daniel Ip, Jian-Piao Cai, Vincent Chi-Chung Cheng, Honglin Chen, Christopher Kim-Ming Hui, and Kwok-Yung Yuen. 2020. A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: A study of a family cluster. Lancet 395, 10223 (Feb. 2020), 514–523. DOI:https://doi.org/10.1016/s0140-6736(20)30154-9

[11]

K. DeviPriya and Sumalatha Lingamgunta. 2020. Multi factor two-way hash-based authentication in cloud computing. Int. J. Cloud Applic. Comput. 10, 2 (2020), 56–76.

Digital Library

[12]

A. George and P. Veeramani. 1994. On some results in fuzzy metric spaces. Fuzzy Sets Syst. 64, 3 (June 1994), 395–399. DOI:https://doi.org/10.1016/0165-0114(94)90162-7

Digital Library

[13]

Mohamed Hammad, Monagi H. Alkinani, B. B. Gupta, and Ahmed A. Abd El-Latif. 2021. Myocardial infarction detection based on deep neural network on imbalanced data. Multim. Syst. (2021), 1–13.

[14]

Ezz El-Din Hemdan, Marwa A. Shouman, and Mohamed Esmail Karar. 2020. COVIDX-Net: A framework of deep learning classifiers to diagnose Covid-19 in X-ray images. arXiv preprint arXiv:2003.11055 (2020).

[15]

M. Shamim Hossain. 2015. Cloud-supported cyber–physical localization framework for patients monitoring. IEEE Syst. J. 11, 1 (2015), 118–127.

[16]

M. Shamim Hossain, Ghulam Muhammad, and Nadra Guizani. 2020. Explainable AI and mass surveillance system-based healthcare framework to combat COVID-19 like pandemics. IEEE Netw 34, 4 (2020), 126–132.

[17]

Srinidhi Jha, Manish Kumar Goyal, Brij Gupta, and Anil Kumar Gupta. 2021. A novel analysis of COVID-19 risk in India incorporating climatic and socio-economic factors. Technol. Forecast. Soc. Change (2021), 120679.

[18]

Weifang Kong and Prachi P. Agarwal. 2020. Chest imaging appearance of COVID-19 infection. Radiol.: Cardiothor. Imag. 2, 1 (Jan. 2020), e200028. DOI:https://doi.org/10.1148/ryct.2020200028

[19]

Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. 2015. Deep learning. Nature 521, 7553 (May 2015), 436–444. DOI:https://doi.org/10.1038/nature14539

[20]

Mehedi Masud, Gurjot Singh Gaba, Salman Alqahtani, Ghulam Muhammad, B. B. Gupta, Pardeep Kumar, and Ahmed Ghoneim. 2020. A lightweight and robust secure key establishment protocol for internet of medical things in COVID-19 patients care. IEEE Internet Things J. (2020).

[21]

Samuel Morillas, Valentín Gregori, Guillermo Peris-Fajarnés, and Pedro Latorre. 2005. A new vector median filter based on fuzzy metrics. In Lecture Notes in Computer Science. Springer Berlin, 81–90. DOI:https://doi.org/10.1007/11559573_11

Digital Library

[22]

Ali Narin, Ceren Kaya, and Ziynet Pamuk. 2020. Automatic detection of coronavirus disease (COVID-19) using x-ray images and deep convolutional neural networks. arXiv preprint arXiv:2003.10849 (2020).

[23]

Abdelwahhab Satta and Sihem Mostefai. 2020. Strategic outsourcing to cloud computing: A comprehensive framework based on analytic hierarchy process. Int. J. Cloud Applic. Comput. 10, 1 (2020), 11–27.

Digital Library

[24]

Ahmed Sedik, Mohamed Hammad, Fathi E. Abd El-Samie, Brij B. Gupta, and Ahmed A. Abd El-Latif. 2021. Efficient deep learning approach for augmented detection of Coronavirus disease. Neural Comput. Applic. (2021), 1–18.

[25]

Ahmed Sedik, Abdullah M. Iliyasu, Abd El-Rahiem, Mohammed E. Abdel Samea, Asmaa Abdel-Raheem, Mohamed Hammad, Jialiang Peng, Abd El-Samie, E. Fathi, Ahmed A. Abd El-Latif et al. 2020. Deploying machine and deep learning models for efficient data-augmented detection of COVID-19 infections. Viruses 12, 7 (2020), 769.

[26]

Prabira Kumar Sethy and Santi Kumari Behera. 2020. Detection of coronavirus disease (COVID-19) based on deep features. (Mar. 2020). Preprints 2020.

[27]

C. Tomasi and R. Manduchi. 1998. Bilateral filtering for gray and color images. In 6th International Conference on Computer Vision (IEEE Cat. No.98CH36271). Narosa Publishing House. https://doi.org/10.1109/iccv.1998.710815

Digital Library

[28]

Haoxiang Wang, Zhihui Li, Yang Li, B. B. Gupta, and Chang Choi. 2020. Visual saliency guided complex image retrieval. Pattern Recog. Lett. 130 (2020), 64–72.

[29]

Linda Wang and Alexander Wong. 2020. COVID-Net: A tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-Ray images. arXiv preprint arXiv:2003.09871 (2020).

[30]

Fan Wu, Su Zhao, Bin Yu, Yan-Mei Chen, Wen Wang, Zhi-Gang Song, Yi Hu, Zhao-Wu Tao, Jun-Hua Tian, Yuan-Yuan Pei, Ming-Li Yuan, Yu-Ling Zhang, Fa-Hui Dai, Yi Liu, Qi-Min Wang, Jiao-Jiao Zheng, Lin Xu, Edward C. Holmes, and Yong-Zhen Zhang. 2020. A new coronavirus associated with human respiratory disease in China. Nature 579, 7798 (Feb. 2020), 265–269. DOI:https://doi.org/10.1038/s41586-020-2008-3

[31]

Jianpeng Zhang, Yutong Xie, Qi Wu, and Yong Xia. 2018. Skin lesion classification in dermoscopy images using synergic deep learning. In Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. Springer International Publishing, 12–20. DOI:https://doi.org/10.1007/978-3-030-00934-2_2

[32]

Zi Yue Zu, Meng Di Jiang, Peng Peng Xu, Wen Chen, Qian Qian Ni, Guang Ming Lu, and Long Jiang Zhang. 2020. Coronavirus disease 2019 (COVID-19): A perspective from China. Radiology 296, 2 (Aug. 2020), E15–E25. DOI:https://doi.org/10.1148/radiol.2020200490

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Index Terms

Synergic Deep Learning for Smart Health Diagnosis of COVID-19 for Connected Living and Smart Cities
1. Information systems
  1. Information systems applications

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Published In

cover image ACM Transactions on Internet Technology

ACM Transactions on Internet Technology Volume 22, Issue 3

August 2022

631 pages

ISSN:1533-5399

EISSN:1557-6051

DOI:10.1145/3498359

Editor:
Ling Liu
Georgia Institute of Technology, USA

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Publication History

Published: 29 November 2021

Accepted: 01 February 2021

Revised: 01 November 2020

Received: 01 September 2020

Published in TOIT Volume 22, Issue 3

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Juárez-Varón DGaón RMengual-Recuerda AVera-Sepúlveda C(2024)Neurotechnologies Applied to Society's Perception of Cyber-Physical Systems (CPS) in Smart CitiesInternational Journal on Semantic Web & Information Systems10.4018/IJSWIS.33594720:1(1-32)Online publication date: 30-Jan-2024
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Mishra KMishra ABansal R(2024)Impact of Technology and Governance on Consumer BehaviourInternational Journal of Digital Strategy, Governance, and Business Transformation10.4018/IJDSGBT.33591713:1(1-13)Online publication date: 12-Jan-2024
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