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Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet

Author

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  • Panwar, Harsh
  • Gupta, P.K.
  • Siddiqui, Mohammad Khubeb
  • Morales-Menendez, Ruben
  • Singh, Vaishnavi

Abstract

Presently, COVID-19 has posed a serious threat to researchers, scientists, health professionals, and administrations around the globe from its detection to its treatment. The whole world is witnessing a lockdown like situation because of COVID-19 pandemic. Persistent efforts are being made by the researchers to obtain the possible solutions to control this pandemic in their respective areas. One of the most common and effective methods applied by the researchers is the use of CT-Scans and X-rays to analyze the images of lungs for COVID-19. However, it requires several radiology specialists and time to manually inspect each report which is one of the challenging tasks in a pandemic. In this paper, we have proposed a deep learning neural network-based method nCOVnet, an alternative fast screening method that can be used for detecting the COVID-19 by analyzing the X-rays of patients which will look for visual indicators found in the chest radiography imaging of COVID-19 patients.

Suggested Citation

  • Panwar, Harsh & Gupta, P.K. & Siddiqui, Mohammad Khubeb & Morales-Menendez, Ruben & Singh, Vaishnavi, 2020. "Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    • Handle: RePEc:eee:chsofr:v:138:y:2020:i:c:s096007792030343x
    DOI: 10.1016/j.chaos.2020.109944
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    References listed on IDEAS

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    1. Fanelli, Duccio & Piazza, Francesco, 2020. "Analysis and forecast of COVID-19 spreading in China, Italy and France," Chaos, Solitons & Fractals, Elsevier, vol. 134(C).
    2. Chimmula, Vinay Kumar Reddy & Zhang, Lei, 2020. "Time series forecasting of COVID-19 transmission in Canada using LSTM networks," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Srinka Basu & Sugata Sen, 2023. "COVID 19 Pandemic, Socio-Economic Behaviour and Infection Characteristics: An Inter-Country Predictive Study Using Deep Learning," Computational Economics, Springer;Society for Computational Economics, vol. 61(2), pages 645-676, February.
    2. Prabhjot Kaur & Shilpi Harnal & Rajeev Tiwari & Fahd S. Alharithi & Ahmed H. Almulihi & Irene Delgado Noya & Nitin Goyal, 2021. "A Hybrid Convolutional Neural Network Model for Diagnosis of COVID-19 Using Chest X-ray Images," IJERPH, MDPI, vol. 18(22), pages 1-17, November.
    3. Toraman, Suat & Alakus, Talha Burak & Turkoglu, Ibrahim, 2020. "Convolutional capsnet: A novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    4. Dingding Wang & Jiaqing Mo & Gang Zhou & Liang Xu & Yajun Liu, 2020. "An efficient mixture of deep and machine learning models for COVID-19 diagnosis in chest X-ray images," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-15, November.
    5. Panwar, Harsh & Gupta, P.K. & Siddiqui, Mohammad Khubeb & Morales-Menendez, Ruben & Bhardwaj, Prakhar & Singh, Vaishnavi, 2020. "A deep learning and grad-CAM based color visualization approach for fast detection of COVID-19 cases using chest X-ray and CT-Scan images," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    6. Younes Bouchlaghem & Yassine Akhiat & Kaouthar Touchanti & Souad Amjad, 2023. "LSAE: Autoencoder Latent Space for Dimensionality Reduction-Based Approach for COVID-19 Classification and Detection Task Using Chest X-ray," SN Operations Research Forum, Springer, vol. 4(4), pages 1-23, December.
    7. Mustafa Abdul Salam & Sanaa Taha & Mohamed Ramadan, 2021. "COVID-19 detection using federated machine learning," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-25, June.
    8. Ben-Loghfyry, Anouar & Charkaoui, Abderrahim, 2023. "Regularized Perona & Malik model involving Caputo time-fractional derivative with application to image denoising," Chaos, Solitons & Fractals, Elsevier, vol. 175(P1).
    9. Yan, Tao & Wong, Pak Kin & Ren, Hao & Wang, Huaqiao & Wang, Jiangtao & Li, Yang, 2020. "Automatic distinction between COVID-19 and common pneumonia using multi-scale convolutional neural network on chest CT scans," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    10. Tayarani N., Mohammad-H., 2021. "Applications of artificial intelligence in battling against covid-19: A literature review," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    11. Mohammad Khishe & Fabio Caraffini & Stefan Kuhn, 2021. "Evolving Deep Learning Convolutional Neural Networks for Early COVID-19 Detection in Chest X-ray Images," Mathematics, MDPI, vol. 9(9), pages 1-18, April.
    12. Jonathan S. Talahua & Jorge Buele & P. Calvopiña & José Varela-Aldás, 2021. "Facial Recognition System for People with and without Face Mask in Times of the COVID-19 Pandemic," Sustainability, MDPI, vol. 13(12), pages 1-19, June.

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