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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.


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.


Cite This Article

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.
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
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.

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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