Authors: Shahnawaz Khan; K. Thirunavukkarasu; Rawad Hammad; Vikram Bali; Mohammed Redha Qader
Addresses: Department of Information Technology, University College of Bahrain, Manama – 55040, Bahrain ' Department of Computer Science and Engineering, Galgotias University, Greater Noida, Uttar Pradesh – 203201, India ' School of Architecture, Computing and Engineering, University of East London, London, E16 2RD, UK ' Computer Science and Engineering Department, JSS Academy of Technical Education, Noida, Uttar Pradesh – 201301, India ' Deanship of Scientific Research, University of Bahrain, Manama – 55040, Bahrain
Abstract: The COVID-19 disease caused by the SARS-CoV-2 infection has widely spread round the globe. Due to the large number of infected cases and rapid spread, it has been declared a global pandemic by World Health Organization on March 2020. There are several methods that identify and detect the COVID patient. However, detection using these methods can be confirmed after up to 10 days of the infection. This research presents a convolutional neural network (CNN) based classification model for detecting a COVID patient using CT image of patient. The dataset, used for the study, consists of CT images of variable sizes. It is a challenge for building a CNN model for variable sizes of the input image. This research uses a hybrid technique to overcome this challenge. It employs and analyses three different methods (such as Adam optimiser, Stochastic gradient descent with momentum optimiser, and RMSProp optimiser) for building the CNN model. Among the three CNN models, for CT image-based classification for infected or non-infected patient, adam performs better than RMSprop and sgdm. The classification accuracy achieved using adam is 94.9%, while RMSprop achieved an accuracy of 91.8% and sgdm reached 93.1%.
Keywords: deep learning; CNN; convolutional neural network; classification; covid19; SARS-CoV-2; image classification.
International Journal of Intelligent Engineering Informatics, 2021 Vol.9 No.2, pp.211 - 228
Received: 07 Sep 2020
Accepted: 09 Mar 2021
Published online: 30 Jul 2021 *