Version 1
: Received: 14 April 2020 / Approved: 16 April 2020 / Online: 16 April 2020 (05:23:06 CEST)
Version 2
: Received: 29 April 2020 / Approved: 30 April 2020 / Online: 30 April 2020 (05:23:32 CEST)
Version 3
: Received: 3 May 2020 / Approved: 5 May 2020 / Online: 5 May 2020 (04:14:58 CEST)
Loey, M.; Manogaran, G.; Khalifa, N.E.M. A Deep Transfer Learning Model with Classical Data Augmentation and CGAN to Detect COVID-19 from Chest CT Radiography Digital Images. Neural Computing and Applications 2020, doi:10.1007/s00521-020-05437-x.
Loey, M.; Manogaran, G.; Khalifa, N.E.M. A Deep Transfer Learning Model with Classical Data Augmentation and CGAN to Detect COVID-19 from Chest CT Radiography Digital Images. Neural Computing and Applications 2020, doi:10.1007/s00521-020-05437-x.
Loey, M.; Manogaran, G.; Khalifa, N.E.M. A Deep Transfer Learning Model with Classical Data Augmentation and CGAN to Detect COVID-19 from Chest CT Radiography Digital Images. Neural Computing and Applications 2020, doi:10.1007/s00521-020-05437-x.
Loey, M.; Manogaran, G.; Khalifa, N.E.M. A Deep Transfer Learning Model with Classical Data Augmentation and CGAN to Detect COVID-19 from Chest CT Radiography Digital Images. Neural Computing and Applications 2020, doi:10.1007/s00521-020-05437-x.
Abstract
The coronavirus disease 2019 (COVID-19) is the fastest transmittable virus caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The detection of COVID-19 using artificial intelligence techniques and especially deep learning will help to detect this virus in early stages which will reflect in increasing the opportunities of fast recovery of patients worldwide. This will lead to release the pressure off the healthcare system around the world. In this research, classical data augmentation techniques along with CGAN based on a deep transfer learning model for COVID-19 detection in chest CT scan images will be presented. The limited benchmark datasets for covid-19 especially in chest CT images is the main motivation of this research. The main idea is to collect all the possible images for covid-19 that exists until the very writing of this research and use the classical data augmentations along with CGAN to generate more images to help in the detection of the COVID-19. In this study, five different deep convolutional neural network-based models (AlexNet, VGGNet16, VGGNet19, GoogleNet, and ResNet50) have been selected for the investigation to detect the coronavirus infected patient using chest CT radiographs digital images. The classical data augmentations along with CGAN improve the performance of classification in all selected deep transfer models. The Outcomes show that ResNet50 is the most appropriate deep learning model to detect the COVID-19 from limited chest CT dataset using the classical data augmentation with testing accuracy of 82.91%.
Keywords
COVID-19; 2019 novel coronavirus; SARS-CoV-2; Deep Transfer Learning; Convolutional Neural Network; Machine Learning; CGAN
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Commenter: Mohamed Loey
Commenter's Conflict of Interests: Author
Manuscript updated