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

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.

• Authors: Aniello Castiglione, Pandi Vijayakumar, Michele Nappi, Saima Sadiq, Muhammad Umer
• Journal: IEEE transactions on industrial informatics (IEEE Trans Industr Inform) Vol. 17 Issue 9 Pg. 6480-6488 (Sep 2021) ISSN: 1551-3203 [Print] United States
• PMID: 37981916 (Publication Type: Journal Article)