Stacked-autoencoder-based model for COVID-19 diagnosis on CT images
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Stacked-autoencoder-based model for COVID-19 diagnosis on CT images Daqiu Li 1,2
&
Zhangjie Fu 1,2 & Jun Xu 3
Received: 2 June 2020 / Revised: 28 September 2020 / Accepted: 1 October 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract With the outbreak of COVID-19, medical imaging such as computed tomography (CT) based diagnosis is proved to be an effective way to fight against the rapid spread of the virus. Therefore, it is important to study computerized models for infectious detection based on CT imaging. New deep learning-based approaches are developed for CT assisted diagnosis of COVID-19. However, most of the current studies are based on a small size dataset of COVID-19 CT images as there are less publicly available datasets for patient privacy reasons. As a result, the performance of deep learning-based detection models needs to be improved based on a small size dataset. In this paper, a stacked autoencoder detector model is proposed to greatly improve the performance of the detection models such as precision rate and recall rate. Firstly, four autoencoders are constructed as the first four layers of the whole stacked autoencoder detector model being developed to extract better features of CT images. Secondly, the four autoencoders are cascaded together and connected to the dense layer and the softmax classifier to constitute the model. Finally, a new classification loss function is constructed by superimposing reconstruction loss to enhance the detection accuracy of the model. The experiment results show that our model is performed well on a small size COVID-2019 CT image dataset. Our model achieves the average accuracy, precision, recall, and F1-score rate of 94.7%, 96.54%, 94.1%, and 94.8%, respectively. The results reflect the ability of our model in discriminating COVID-19 images which might help radiologists in the diagnosis of suspected COVID-19 patients. Keywords COVID-19 diagnosis . Computed tomography . Deep learning . Stacked autoencoder
1 Introduction Coronavirus severe acute respiratory syndrome (SARS)-CoV2 broke out in December 2019. All patients infected with COVID-19 virus developed symptoms of mild or severe respiratory disease COVID-19 [1, 2]. In the following months, COVID-19 spreads rapidly around the world. On March 11, 2020, the World Health Organization declared COVID-19 disease to be a global pandemic [3, 4]. The inefficiency of the global detection of the disease is one of the reasons for its rapid spread [5]. Since the isolation and genome
* Zhangjie Fu [email protected] 1
School of Computer and Software, Nanjing University of Information Science & Technology, Nanjing 210044, China
2
Peng Cheng Laboratory, Shenzhen 518000, China
3
School of Automation, Nanjing University of Information Science & Technology, Nanjing 210044, China
sequencing of COVID-19 virus [6, 7], the current diagnostic methods for detection of COVID-19 virus include nucleic acid detection kit method (TR-PCR method) and COVID-19 nucleic acid sequencing method. However, TR-PCR
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