Strong semantic segmentation for Covid-19 detection: Evaluating the use of deep learning models as a performant tool in radiography.
With the increasing number of Covid-19 cases as well as care costs, chest diseases have gained increasing interest in several communities, particularly in medical and computer vision. Clinical and analytical exams are widely recognized techniques for diagnosing and handling Covid-19 cases. However,...
| Publicado en: | Radiography Vol. 29; no. 1; pp. 109 - 119 |
|---|---|
| Autores principales: | , , |
| Formato: | research Journal Article |
| Publicado: |
W B Saunders
Jan2023
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=161324980&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 161324980 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10788174 DPD jtl: Radiography issn: 10788174 maglogo: N pubinfo: dt: Jan2023 vid: 29 iid: 1 pid: 1351 pub: W B Saunders place: Philadelphia, Pennsylvania artinfo: ui: 161324980 161324980 161324980 10.1016/j.radi.2022.10.010 161324980 ppf: 109 ppct: 10 formats: tig: atl: Strong semantic segmentation for Covid-19 detection: Evaluating the use of deep learning models as a performant tool in radiography. aug: au: Allioui, H. Mourdi, Y. Sadgal, M. affil: Computer Sciences Department, Faculty of Sciences Semlalia, Cadi Ayyad University, Morocco sug: subj: COVID-19 Diagnosis Deep Learning Methods Image Processing, Computer Assisted Methods Semantics Human Thorax Pathology COVID-19 Economics Health Care Costs COVID-19 Symptoms Time Tomography, X-Ray Computed Radiography, Thoracic Neural Networks (Computer) Precision Sensitivity and Specificity Radiologic Technologists Physicians Automation ab: With the increasing number of Covid-19 cases as well as care costs, chest diseases have gained increasing interest in several communities, particularly in medical and computer vision. Clinical and analytical exams are widely recognized techniques for diagnosing and handling Covid-19 cases. However, strong detection tools can help avoid damage to chest tissues. The proposed method provides an important way to enhance the semantic segmentation process using combined potential deep learning (DL) modules to increase consistency. Based on Covid-19 CT images, this work hypothesized that a novel model for semantic segmentation might be able to extract definite graphical features of Covid-19 and afford an accurate clinical diagnosis while optimizing the classical test and saving time. CT images were collected considering different cases (normal chest CT, pneumonia, typical viral causes, and Covid-19 cases). The study presents an advanced DL method to deal with chest semantic segmentation issues. The approach employs a modified version of the U-net to enable and support Covid-19 detection from the studied images. The validation tests demonstrated competitive results with important performance rates: Precision (90.96% ± 2.5) with an F-score of (91.08% ± 3.2), an accuracy of (93.37% ± 1.2), a sensitivity of (96.88% ± 2.8) and a specificity of (96.91% ± 2.3). In addition, the visual segmentation results are very close to the Ground truth. The findings of this study reveal the proof-of-principle for using cooperative components to strengthen the semantic segmentation modules for effective and truthful Covid-19 diagnosis. This paper has highlighted that DL based approach, with several modules, may be contributing to provide strong support for radiographers and physicians, and that further use of DL is required to design and implement performant automated vision systems to detect chest diseases. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|