Feature fusion based VGGFusionNet model to detect COVID-19 patients utilizing computed tomography scan images
Issued Date
2022-12-01
Resource Type
eISSN
20452322
Scopus ID
2-s2.0-85144192163
Pubmed ID
36526680
Journal Title
Scientific Reports
Volume
12
Issue
1
Rights Holder(s)
SCOPUS
Bibliographic Citation
Scientific Reports Vol.12 No.1 (2022)
Suggested Citation
Uddin K.M.M., Dey S.K., Babu H.M.H., Mostafiz R., Uddin S., Shoombuatong W., Moni M.A. Feature fusion based VGGFusionNet model to detect COVID-19 patients utilizing computed tomography scan images. Scientific Reports Vol.12 No.1 (2022). doi:10.1038/s41598-022-25539-x Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/86363
Title
Feature fusion based VGGFusionNet model to detect COVID-19 patients utilizing computed tomography scan images
Other Contributor(s)
Abstract
COVID-19 is one of the most life-threatening and dangerous diseases caused by the novel Coronavirus, which has already afflicted a larger human community worldwide. This pandemic disease recovery is possible if detected in the early stage. We proposed an automated deep learning approach from Computed Tomography (CT) scan images to detect COVID-19 positive patients by following a four-phase paradigm for COVID-19 detection: preprocess the CT scan images; remove noise from test image by using anisotropic diffusion techniques; make a different segment for the preprocessed images; and train and test COVID-19 detection using Convolutional Neural Network (CNN) models. This study employed well-known pre-trained models, including AlexNet, ResNet50, VGG16 and VGG19 to evaluate experiments. 80% of images are used to train the network in the detection process, while the remaining 20% are used to test it. The result of the experiment evaluation confirmed that the VGG19 pre-trained CNN model achieved better accuracy (98.06%). We used 4861 real-life COVID-19 CT images for experiment purposes, including 3068 positive and 1793 negative images. These images were acquired from a hospital in Sao Paulo, Brazil and two other different data sources. Our proposed method revealed very high accuracy and, therefore, can be used as an assistant to help professionals detect COVID-19 patients accurately.