Lung Infection Segmentation for COVID-19 Pneumonia Based on a Cascade Convolutional Network from CT Images.
The COVID-19 pandemic is a global, national, and local public health concern which has caused a significant outbreak in all countries and regions for both males and females around the world. Automated detection of lung infections and their boundaries from medical images offers a great potential to a...
| Published in: | BioMed Research International pp. 1 - 17 |
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| Main Authors: | , , , , , , , |
| Format: | diagnostic images equations & formulas research tables/charts Journal Article |
| Published: |
Wiley-Blackwell
4/16/2021
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=149835981&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 149835981 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 4/16/2021 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 149835981 149835981 149835981 10.1155/2021/5544742 149835981 ppf: 1 ppct: 16 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Lung Infection Segmentation for COVID-19 Pneumonia Based on a Cascade Convolutional Network from CT Images. aug: au: Ranjbarzadeh, Ramin Jafarzadeh Ghoushchi, Saeid Bendechache, Malika Amirabadi, Amir Ab Rahman, Mohd Nizam Baseri Saadi, Soroush Aghamohammadi, Amirhossein Kooshki Forooshani, Mersedeh affil: Department of Telecommunications Engineering, Faculty of Engineering, University of Guilan, Rasht, Iran sug: subj: COVID-19 Radiography Lung Radiography Infection Diagnosis Tomography, X-Ray Computed Methods Image Interpretation, Computer Assisted Predictive Value of Tests Human Neural Networks (Computer) COVID-19 Classification Sensitivity and Specificity Descriptive Statistics ab: The COVID-19 pandemic is a global, national, and local public health concern which has caused a significant outbreak in all countries and regions for both males and females around the world. Automated detection of lung infections and their boundaries from medical images offers a great potential to augment the patient treatment healthcare strategies for tackling COVID-19 and its impacts. Detecting this disease from lung CT scan images is perhaps one of the fastest ways to diagnose patients. However, finding the presence of infected tissues and segment them from CT slices faces numerous challenges, including similar adjacent tissues, vague boundary, and erratic infections. To eliminate these obstacles, we propose a two-route convolutional neural network (CNN) by extracting global and local features for detecting and classifying COVID-19 infection from CT images. Each pixel from the image is classified into the normal and infected tissues. For improving the classification accuracy, we used two different strategies including fuzzy c -means clustering and local directional pattern (LDN) encoding methods to represent the input image differently. This allows us to find more complex pattern from the image. To overcome the overfitting problems due to small samples, an augmentation approach is utilized. The results demonstrated that the proposed framework achieved precision 96%, recall 97%, F score, average surface distance (ASD) of 2.8 ± 0.3 mm, and volume overlap error (VOE) of 5.6 ± 1.2 %. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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