@Article{cmc.2022.019876, AUTHOR = {A. Sheryl Oliver, P. Suresh, A. Mohanarathinam, Seifedine Kadry, Orawit Thinnukool}, TITLE = {Optimal Deep Dense Convolutional Neural Network Based Classification Model for COVID-19 Disease}, JOURNAL = {Computers, Materials \& Continua}, VOLUME = {70}, YEAR = {2022}, NUMBER = {1}, PAGES = {2031--2047}, URL = {http://www.techscience.com/cmc/v70n1/44442}, ISSN = {1546-2226}, 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.}, DOI = {10.32604/cmc.2022.019876} }