A deep learning-based diagnosis system for COVID-19 detection and pneumonia screening using CT imaging

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dc.contributor.authorMahmoudi, Ramzi
dc.contributor.authorBenameur, Narjes
dc.contributor.authorMabrouk, Rania
dc.contributor.authorMohammed, Mazin Abed
dc.contributor.authorGarcía-Zapirain, Begoña
dc.contributor.authorBedoui, Mohamed Hédi
dc.date.accessioned2025-06-16T09:02:58Z
dc.date.available2025-06-16T09:02:58Z
dc.date.issued2022-05-10
dc.date.updated2025-06-16T09:02:58Z
dc.description.abstractSevere Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a global threat impacting the lives of millions of people worldwide. Automated detection of lung infections from Computed Tomography scans represents an excellent alternative; however, segmenting infected regions from CT slices encounters many challenges. Developing a diagnosis system based on deep learning techniques to detect and quantify COVID-19 infection and pneumonia screening using CT imaging. Contrast Limited Adaptive Histogram Equalization pre-processing method was used to remove the noise and intensity in homogeneity. Black slices were also removed to crop only the region of interest containing the lungs. A U-net architecture, based on CNN encoder and CNN decoder approaches, is then introduced for a fast and precise image segmentation to obtain the lung and infection segmentation models. For better estimation of skill on unseen data, a fourfold cross-validation as a resampling procedure has been used. A three-layered CNN architecture, with additional fully connected layers followed by a Softmax layer, was used for classification. Lung and infection volumes have been reconstructed to allow volume ratio computing and obtain infection rate. Starting with the 20 CT scan cases, data has been divided into 70% for the training dataset and 30% for the validation dataset. Experimental results demonstrated that the proposed system achieves a dice score of 0.98 and 0.91 for the lung and infection segmentation tasks, respectively, and an accuracy of 0.98 for the classification task. The proposed workflow aimed at obtaining good performances for the different system’s components, and at the same time, dealing with reduced datasets used for training.en
dc.description.sponsorshipThe Francophone University Agency (AUF) COVID-19.1 PANDEMIC SPECIAL PLAN. Project ID 469, Title: Development and implementation of a decision support system based on artificial intelligence applied to medical imaging to improve the management of patients with SARS-CoV2 (SPECTRUM) (2020)en
dc.identifier.citationMahmoudi, R., Benameur, N., Mabrouk, R., Mohammed, M. A., Garcia-Zapirain, B., & Bedoui, M. H. (2022). A deep learning-based diagnosis system for COVID-19 detection and pneumonia screening using CT imaging. Applied Sciences, 12(10). https://doi.org/10.3390/APP12104825
dc.identifier.doi10.3390/APP12104825
dc.identifier.eissn2076-3417
dc.identifier.urihttp://hdl.handle.net/20.500.14454/3062
dc.language.isoeng
dc.publisherMDPI
dc.rights© 2022 by the authors
dc.subject.other3D reconstruction
dc.subject.otherClassification
dc.subject.otherCT scans
dc.subject.otherDeep learning
dc.subject.otherInfection segmentation
dc.subject.otherLung segmentation
dc.subject.otherSARS-CoV-2
dc.titleA deep learning-based diagnosis system for COVID-19 detection and pneumonia screening using CT imagingen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.issue10
oaire.citation.titleApplied Sciences
oaire.citation.volume12
oaire.licenseConditionhttps://creativecommons.org/licenses/by/4.0/
oaire.versionVoR
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