Vol.70, No.1, 2022, pp.2031-2047, doi:10.32604/cmc.2022.019876
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:
Received 29 April 2021; Accepted 08 June 2021; Issue published 07 September 2021
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.
Deep learning; deep dense convolutional neural network; covid-19; CT images; chimp optimization algorithm
Cite This Article
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. CMC-Computers, Materials & Continua, 70(1), 2031–2047.
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