DIAG a diagnostic web application based on lung CT Scan images and deep learning

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dc.contributor.authorHadj Bouzid, Amel Imene
dc.contributor.authorYahiaoui, Saïd
dc.contributor.authorLounis, Anis
dc.contributor.authorBerrani, Sid-Ahmed
dc.contributor.authorBelbachir, Hacène
dc.contributor.authorNaili, Qaid
dc.contributor.authorAbdi, Mohamed El Hafedh
dc.contributor.authorBensalah, Kawthar
dc.contributor.authorBelazzougui, Djamal
dc.date.accessioned2023-10-16T15:26:29Z
dc.date.available2023-10-16T15:26:29Z
dc.date.issued2021-05-29
dc.description.abstractCoronavirus disease is a pandemic that has infected millions of people around the world. Lung CT-scans are effective diagnostic tools, but radiologists can quickly become overwhelmed by the flow of infected patients. Therefore, automated image interpretation needs to be achieved. Deep learning (DL) can support critical medical tasks including diagnostics, and DL algorithms have successfully been applied to the classification and detection of many diseases. This work aims to use deep learning methods that can classify patients between Covid-19 positive and healthy patient. We collected 4 available datasets, and tested our convolutional neural networks (CNNs) on different distributions to investigate the generalizability of our models. In order to clearly explain the predictions, Grad-CAM and Fast-CAM visualization methods were used. Our approach reaches more than 92% accuracy on 2 different distributions. In addition, we propose a computer aided diagnosis web application for Covid-19 diagnosis. The results suggest that our proposed deep learning tool can be integrated to the Covid-19 detection process and be useful for a rapid patient management.
dc.identifier.isbn978-1-64368-184-9
dc.identifier.urihttps://dl.cerist.dz/handle/CERIST/995
dc.publisherIOS Press Ebooks
dc.relation.ispartofseriesMIE 2021, 31st Medical Informatics Europe Conference
dc.relation.pages332-336
dc.relation.placeVirtual event
dc.structureCAlcul Parallèle et Applications
dc.subjectCovid-19
dc.subjectCT-scan
dc.subjectDeep learning
dc.subjectCNN
dc.subjectClassification
dc.titleDIAG a diagnostic web application based on lung CT Scan images and deep learning
dc.typeConference paper
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