Feature fusion based VGGFusionNet model to detect COVID-19 patients utilizing computed tomography scan images

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 Compute...

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Bibliographic Details
Published in:Scientific reports 2022-12, Vol.12 (1), p.21796-21796, Article 21796
Main Authors: Uddin, Khandaker Mohammad Mohi, Dey, Samrat Kumar, Babu, Hafiz Md. Hasan, Mostafiz, Rafid, Uddin, Shahadat, Shoombuatong, Watshara, Moni, Mohammad Ali
Format: Article
Language:English
Subjects:
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Brazil
Computed tomography
Coronaviruses
COVID-19
COVID-19 - diagnostic imaging
Deep learning
Humanities and Social Sciences
Humans
multidisciplinary
Neural networks
Pandemics
Patients
Radionuclide Imaging
Science
Science (multidisciplinary)
Tomography
Tomography, X-Ray Computed
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Summary: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.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-022-25539-x