tetano
Editor, Senior Moderator
Sci Rep
. 2022 Dec 16;12(1):21796.
doi: 10.1038/s41598-022-25539-x.
Feature fusion based VGGFusionNet model to detect COVID-19 patients utilizing computed tomography scan images
Khandaker Mohammad Mohi Uddin[SUP] 1 [/SUP], Samrat Kumar Dey[SUP] 2 [/SUP], Hafiz Md Hasan Babu[SUP] 3 [/SUP], Rafid Mostafiz[SUP] 4 [/SUP], Shahadat Uddin[SUP] 5 [/SUP], Watshara Shoombuatong[SUP] 6 [/SUP], Mohammad Ali Moni[SUP] 7 [/SUP]
Affiliations
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.
. 2022 Dec 16;12(1):21796.
doi: 10.1038/s41598-022-25539-x.
Feature fusion based VGGFusionNet model to detect COVID-19 patients utilizing computed tomography scan images
Khandaker Mohammad Mohi Uddin[SUP] 1 [/SUP], Samrat Kumar Dey[SUP] 2 [/SUP], Hafiz Md Hasan Babu[SUP] 3 [/SUP], Rafid Mostafiz[SUP] 4 [/SUP], Shahadat Uddin[SUP] 5 [/SUP], Watshara Shoombuatong[SUP] 6 [/SUP], Mohammad Ali Moni[SUP] 7 [/SUP]
Affiliations
- PMID: 36526680
- DOI: 10.1038/s41598-022-25539-x
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.