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dc.contributor.authorKhattak, Muhammad Irfan
dc.contributor.authorAl-Hasan, Mu'ath
dc.contributor.authorJan, Atif
dc.contributor.authorSaleem, Nasir
dc.contributor.authorVerdú, Elena
dc.contributor.authorKhurshid, Numan
dc.date2021
dc.date.accessioned2022-01-17T11:57:57Z
dc.date.available2022-01-17T11:57:57Z
dc.identifier.issn1989-1660
dc.identifier.urihttps://reunir.unir.net/handle/123456789/12318
dc.description.abstractThe novel coronavirus-2019 (Covid-19), a contagious disease became a pandemic and has caused overwhelming effects on the human lives and world economy. The detection of the contagious disease is vital to avert further spread and to promptly treat the infected people. The need of automated scientific assisting diagnostic methods to identify Covid-19 in the infected people has increased since less accurate automated diagnostic methods are available. Recent studies based on the radiology imaging suggested that the imaging patterns on X-ray images and Computed Tomography (CT) scans contain leading information about Covid-19 and is considered as a potential automated diagnosis method. Machine learning and deep learning techniques combined with radiology imaging can be helpful for accurate detection of the disease. A deep learning approach based on the multilayer-Spatial Convolutional Neural Network for automatic detection of Covid-19 using chest X-ray images and CT scans is proposed in this paper. The proposed model, named as the Multilayer Spatial Covid Convolutional Neural Network (MSCovCNN), provides an automated accurate diagnostics for Covid-19 detection. The proposed model showed 93.63% detection accuracy and 97.88% AUC (Area Under Curve) for chest x-ray images and 91.44% detection accuracy and 95.92% AUC for chest CT scans, respectively. We have used 5-tiered 2D-CNN frameworks followed by the Artificial Neural Network (ANN) and softmax classifier. In the CNN each convolution layer is followed by an activation function and a Maxpooling layer. The proposed model can be used to assist the radiologists in detecting the Covid-19 and confirming their initial screening.es_ES
dc.language.isoenges_ES
dc.publisherInternational Journal of Interactive Multimedia and Artificial Intelligencees_ES
dc.relation.ispartofseries;vol. 6, nº 6
dc.relation.urihttps://www.ijimai.org/journal/bibcite/reference/2928es_ES
dc.rightsopenAccesses_ES
dc.subjectCOVID-19es_ES
dc.subjectmachine learninges_ES
dc.subjectconvolutional neurales_ES
dc.subjectnetworkes_ES
dc.subjectX-rays Imageses_ES
dc.subjectCT Scanses_ES
dc.subjectWOS(2)es_ES
dc.subjectScopuses_ES
dc.titleAutomated Detection of COVID-19 using Chest X-Ray Images and CT Scans through Multilayer-Spatial Convolutional Neural Networkses_ES
dc.typearticlees_ES
reunir.tag~ARIes_ES
dc.identifier.doihttp://doi.org/10.9781/ijimai.2020.06.002


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