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Multi-Modality and Feature Fusion-Based COVID-19 Detection Through Long Short-Term Memory

Noureen Fatima1, Rashid Jahangir2, Ghulam Mujtaba1, Adnan Akhunzada3,*, Zahid Hussain Shaikh4, Faiza Qureshi1

1 Center of Excellence for Robotics, Artificial Intelligence, and Blockchain, Department of Computer Science, Sukkur IBA University, Sukkur, Pakistan
2 Department of Computer Science, COMSATS University Islamabad–Vehari Campus, Pakistan
3 Faculty of Computing and Informatics, University Malaysia Sabah, Kota Kinabalu, 88400, Malaysia
4 Department of Mathematics, Sukkur IBA University, Sukkur, Pakistan

* Corresponding Author: Adnan Akhunzada. Email: email

Computers, Materials & Continua 2022, 72(3), 4357-4374. https://doi.org/10.32604/cmc.2022.023830

Abstract

The Coronavirus Disease 2019 (COVID-19) pandemic poses the worldwide challenges surpassing the boundaries of country, religion, race, and economy. The current benchmark method for the detection of COVID-19 is the reverse transcription polymerase chain reaction (RT-PCR) testing. Nevertheless, this testing method is accurate enough for the diagnosis of COVID-19. However, it is time-consuming, expensive, expert-dependent, and violates social distancing. In this paper, this research proposed an effective multi-modality-based and feature fusion-based (MMFF) COVID-19 detection technique through deep neural networks. In multi-modality, we have utilized the cough samples, breathe samples and sound samples of healthy as well as COVID-19 patients from publicly available COSWARA dataset. Extensive set of experimental analyses were performed to evaluate the performance of our proposed approach. Several useful features were extracted from the aforementioned modalities that were then fed as an input to long short-term memory recurrent neural network algorithms for the classification purpose. Extensive set of experimental analyses were performed to evaluate the performance of our proposed approach. The experimental results showed that our proposed approach outperformed compared to four baseline approaches published recently. We believe that our proposed technique will assists potential users to diagnose the COVID-19 without the intervention of any expert in minimum amount of time.

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APA Style
Fatima, N., Jahangir, R., Mujtaba, G., Akhunzada, A., Shaikh, Z.H. et al. (2022). Multi-modality and feature fusion-based COVID-19 detection through long short-term memory. Computers, Materials & Continua, 72(3), 4357-4374. https://doi.org/10.32604/cmc.2022.023830
Vancouver Style
Fatima N, Jahangir R, Mujtaba G, Akhunzada A, Shaikh ZH, Qureshi F. Multi-modality and feature fusion-based COVID-19 detection through long short-term memory. Comput Mater Contin. 2022;72(3):4357-4374 https://doi.org/10.32604/cmc.2022.023830
IEEE Style
N. Fatima, R. Jahangir, G. Mujtaba, A. Akhunzada, Z.H. Shaikh, and F. Qureshi "Multi-Modality and Feature Fusion-Based COVID-19 Detection Through Long Short-Term Memory," Comput. Mater. Contin., vol. 72, no. 3, pp. 4357-4374. 2022. https://doi.org/10.32604/cmc.2022.023830



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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