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Automatic Detection of COVID-19 Using a Stacked Denoising Convolutional Autoencoder

Computers, Materials & Continua 2021年第12期69卷 3259-3274页

作者： Habib Dhahri Besma Rabhi slaheddine chelbi Omar Almutiry Awais Mahmood Adel M.Alimi College of Applied Computer Sciences(CACS) King Saud UniversityRiyadh11543Saudi Arabia Faculty of Sciences and Technology of Sidi Bouzid University of KairouanKairouanTunisia National School of Engineers(ENIS) University of SfaxTunisia Department of Computer Sciences College of Sciences and Arts in UnaizahQassim UniversityAl-QassimKingdom of Saudi Arabia

The exponential increase in new coronavirus disease 2019(COVID-19)cases and deaths has made COVID-19 the leading cause of death in many ***,in this study,we propose an efficient technique for the automatic detection of COVID-19 and pneumonia based on X-ray images.A stacked denoising convolutional autoencoder(SDCA)model was proposed to classify X-ray images into three classes:normal,pneumonia,and *** SDCA model was used to obtain a good representation of the input data and extract the relevant features from noisy *** proposed model’s architecture mainly composed of eight autoencoders,which were fed to two dense layers and SoftMax *** proposed model was evaluated with 6356 images from the datasets from different *** experiments and evaluation of the proposed model were applied to an 80/20 training/validation split and for five cross-validation data splitting,*** metrics used for the SDCA model were the classification accuracy,precision,sensitivity,and specificity for both *** results demonstrated the superiority of the proposed model in classifying X-ray images with high accuracy of 96.8%.Therefore,this model can help physicians accelerate COVID-19 diagnosis.

关键词： Stacked autoencoder augmentation multiclassification COVID-19 convolutional neural network

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