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Optimal Deep Dense Convolutional Neural Network Based Classification Model for COVID-19 Disease

A. Sheryl Oliver1, P. Suresh2, A. Mohanarathinam3, Seifedine Kadry4, Orawit Thinnukool5,*

1 Department of Computer Science and Engineering, St. Joseph's College of Engineering, 600119, Chennai, India
2 Department of Computer Science and Engineering, KPR Institute of Engineering and Technology, Coimbatore, 641407, India
3 Faculty of Engineering, Karpagam Academy of Higher Education, Coimbatore, 641021, India
4 Faculty of Applied Computing and Technology, Noroff University College, Kristiansand, 4608, Norway
5 Research Group of Embedded Systems and Mobile Application in Health Science, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai, 50200, Thailand

* Corresponding Author: Orawit Thinnukool. Email: email

Computers, Materials & Continua 2022, 70(1), 2031-2047. https://doi.org/10.32604/cmc.2022.019876

Abstract

Early diagnosis and detection are important tasks in controlling the spread of COVID-19. A number of Deep Learning techniques has been established by researchers to detect the presence of COVID-19 using CT scan images and X-rays. However, these methods suffer from biased results and inaccurate detection of the disease. So, the current research article developed Oppositional-based Chimp Optimization Algorithm and Deep Dense Convolutional Neural Network (OCOA-DDCNN) for COVID-19 prediction using CT images in IoT environment. The proposed methodology works on the basis of two stages such as pre-processing and prediction. Initially, CT scan images generated from prospective COVID-19 are collected from open-source system using IoT devices. The collected images are then pre-processed using Gaussian filter. Gaussian filter can be utilized in the removal of unwanted noise from the collected CT scan images. Afterwards, the pre-processed images are sent to prediction phase. In this phase, Deep Dense Convolutional Neural Network (DDCNN) is applied upon the pre-processed images. The proposed classifier is optimally designed with the consideration of Oppositional-based Chimp Optimization Algorithm (OCOA). This algorithm is utilized in the selection of optimal parameters for the proposed classifier. Finally, the proposed technique is used in the prediction of COVID-19 and classify the results as either COVID-19 or non-COVID-19. The projected method was implemented in MATLAB and the performances were evaluated through statistical measurements. The proposed method was contrasted with conventional techniques such as Convolutional Neural Network-Firefly Algorithm (CNN-FA), Emperor Penguin Optimization (CNN-EPO) respectively. The results established the supremacy of the proposed model.

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APA Style
Oliver, A.S., Suresh, P., Mohanarathinam, A., Kadry, S., Thinnukool, O. (2022). Optimal deep dense convolutional neural network based classification model for COVID-19 disease. Computers, Materials & Continua, 70(1), 2031-2047. https://doi.org/10.32604/cmc.2022.019876
Vancouver Style
Oliver AS, Suresh P, Mohanarathinam A, Kadry S, Thinnukool O. Optimal deep dense convolutional neural network based classification model for COVID-19 disease. Comput Mater Contin. 2022;70(1):2031-2047 https://doi.org/10.32604/cmc.2022.019876
IEEE Style
A.S. Oliver, P. Suresh, A. Mohanarathinam, S. Kadry, and O. Thinnukool "Optimal Deep Dense Convolutional Neural Network Based Classification Model for COVID-19 Disease," Comput. Mater. Contin., vol. 70, no. 1, pp. 2031-2047. 2022. https://doi.org/10.32604/cmc.2022.019876



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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