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A Healthcare System for COVID19 Classification Using Multi-Type Classical Features Selection

Muhammad Attique Khan1, Majed Alhaisoni2, Muhammad Nazir1, Abdullah Alqahtani3, Adel Binbusayyis3, Shtwai Alsubai3, Yunyoung Nam4, Byeong-Gwon Kang4,*

1 Department of Computer Science, HITEC University, Taxila, Pakistan
2 Computer Sciences Department, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University (PNU), P.O. Box 84428, Riyadh, 11671, Saudi Arabia
3 College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia
4 Department of ICT Convergence, Soonchunhyang University, Asan, 31538, Korea

* Corresponding Author: Byeong-Gwon Kang. Email: email

Computers, Materials & Continua 2023, 74(1), 1393-1412. https://doi.org/10.32604/cmc.2023.032064

Abstract

The coronavirus (COVID19), also known as the novel coronavirus, first appeared in December 2019 in Wuhan, China. After that, it quickly spread throughout the world and became a disease. It has significantly impacted our everyday lives, the national and international economies, and public health. However, early diagnosis is critical for prompt treatment and reducing trauma in the healthcare system. Clinical radiologists primarily use chest X-rays, and computerized tomography (CT) scans to test for pneumonia infection. We used Chest CT scans to predict COVID19 pneumonia and healthy scans in this study. We proposed a joint framework for prediction based on classical feature fusion and PSO-based optimization. We begin by extracting standard features such as discrete wavelet transforms (DWT), discrete cosine transforms (DCT), and dominant rotated local binary patterns (DRLBP). In addition, we extracted Shanon Entropy and Kurtosis features. In the following step, a Max-Covariance-based maximization approach for feature fusion is proposed. The fused features are optimized in the preliminary phase using Particle Swarm Optimization (PSO) and the ELM fitness function. For final prediction, PSO is used to obtain robust features, which are then implanted in a Support Vector Data Description (SVDD) classifier. The experiment is carried out using available COVID19 Chest CT Scans and scans from healthy patients. These images are from the Radiopaedia website. For the proposed scheme, the fusion and selection process accuracy is 88.6% and 93.1%, respectively. A detailed analysis is conducted, which supports the proposed system efficiency.

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Cite This Article

APA Style
Khan, M.A., Alhaisoni, M., Nazir, M., Alqahtani, A., Binbusayyis, A. et al. (2023). A healthcare system for COVID19 classification using multi-type classical features selection. Computers, Materials & Continua, 74(1), 1393-1412. https://doi.org/10.32604/cmc.2023.032064
Vancouver Style
Khan MA, Alhaisoni M, Nazir M, Alqahtani A, Binbusayyis A, Alsubai S, et al. A healthcare system for COVID19 classification using multi-type classical features selection. Comput Mater Contin. 2023;74(1):1393-1412 https://doi.org/10.32604/cmc.2023.032064
IEEE Style
M.A. Khan et al., "A Healthcare System for COVID19 Classification Using Multi-Type Classical Features Selection," Comput. Mater. Contin., vol. 74, no. 1, pp. 1393-1412. 2023. https://doi.org/10.32604/cmc.2023.032064



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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