COVID-19: Automatic Detection of the Novel Coronavirus Disease From CT Images Using an Optimized Convolutional Neural Network

Aniello Castiglione; Pandi Vijayakumar; Michele Nappi; Saima Sadiq; Muhammad Umer

doi:10.1109/TII.2021.3057524

COVID-19: Automatic Detection of the Novel Coronavirus Disease From CT Images Using an Optimized Convolutional Neural Network

IEEE Trans Industr Inform. 2021 Feb 5;17(9):6480-6488. doi: 10.1109/TII.2021.3057524. eCollection 2021 Sep.

Authors

Aniello Castiglione¹, Pandi Vijayakumar², Michele Nappi³, Saima Sadiq⁴, Muhammad Umer^{4

5}

Affiliations

¹ Department of Science and TechnologyUniversity of Naples Parthenope 80143 Naples Italy.
² Department of Computer Science and EngineeringUniversity College of Engineering Tindivanam Tindivanam 604001 India.
³ Department of Computer ScienceUniversity of Salerno 84084 Fisciano Italy.
⁴ Department of Computer ScienceKhwaja Fareed University of Engineering and Information Technology Rahim Yar Khan 64200 Pakistan.
⁵ Department of Computer Science and Information TechnologyThe Islamia University of Bahawalpur Bahawalpur 63100 Pakistan.

Abstract

It is widely known that a quick disclosure of the COVID-19 can help to reduce its spread dramatically. Transcriptase polymerase chain reaction could be a more useful, rapid, and trustworthy technique for the evaluation and classification of the COVID-19 disease. Currently, a computerized method for classifying computed tomography (CT) images of chests can be crucial for speeding up the detection while the COVID-19 epidemic is rapidly spreading. In this article, the authors have proposed an optimized convolutional neural network model (ADECO-CNN) to divide infected and not infected patients. Furthermore, the ADECO-CNN approach is compared with pretrained convolutional neural network (CNN)-based VGG19, GoogleNet, and ResNet models. Extensive analysis proved that the ADECO-CNN-optimized CNN model can classify CT images with 99.99% accuracy, 99.96% sensitivity, 99.92% precision, and 99.97% specificity.

Keywords: COVID-19; Convolutional neural network (CNN); computed tomography (CT) images; deep learning; diagnostic imaging.