Abstract
The COVID-19 pandemic continues to spread globally at a rapid pace, and its rapid detection remains a challenge due to its rapid infectivity and limited testing availability. One of the simply available imaging modalities in clinical routine involves chest X-ray (CXR), which is often used for diagnostic purposes. Here, we proposed a computer-aided detection of COVID-19 in CXR imaging using deep and conventional radiomic features. First, we used a 2D U-Net model to segment the lung lobes. Then, we extracted deep latent space radiomics by applying deep convolutional autoencoder (ConvAE) with internal dense layers to extract low-dimensional deep radiomics. We used Johnson–Lindenstrauss (JL) lemma, Laplacian scoring (LS), and principal component analysis (PCA) to reduce dimensionality in conventional radiomics. The generated low-dimensional deep and conventional radiomics were integrated to classify COVID-19 from pneumonia and healthy patients. We used 704 CXR images for training the entire model (i.e., U-Net, ConvAE, and feature selection in conventional radiomics). Afterward, we independently validated the whole system using a study cohort of 1597 cases. We trained and tested a random forest model for detecting COVID-19 cases through multivariate binary-class and multiclass classification. The maximal (full multivariate) model using a combination of the two radiomic groups yields performance in classification cross-validated accuracy of 72.6% (69.4–74.4%) for multiclass and 89.6% (88.4–90.7%) for binary-class classification.
Highlights
IntroductionThe global pandemic associated with COVID-19 continues to spread across the world
Introduction iationsThe global pandemic associated with COVID-19 continues to spread across the world.It has led to more than 151 million cases and 3.17 million deaths as of 30 April 2021, according to the World Health Organization (WHO) statistics [1]
The proposed approach trains and independently uses a 2D U-Net model for segmenting the lung lobes in chest X-ray (CXR) images; We proposed a convolutional deep autoencoder (ConvAE) to extract low-dimensional deep-imaging features, called deep radiomics, from CXR images as potential diagnostic biomarkers for COVID-19; Our study addresses the curse of dimensionality problem using high-dimensional deep radiomics by utilizing a ConvAE to compress the feature space and combine them with conventional radiomics for diagnostic purposes; The proposed model successfully classifies subjects into healthy, pneumonia, and COVID-19 cases through binary- and multiclass classification, as validated with an independent cohort of patients
Summary
The global pandemic associated with COVID-19 continues to spread across the world. It has led to more than 151 million cases and 3.17 million deaths as of 30 April 2021, according to the World Health Organization (WHO) statistics [1]. The WHO declared this to be a Public Health Emergency of International Concern (PHEIC) on 30 January 2020, and on 11 March 2020, the situation was recognized as a global pandemic [2,3]. The highly contagious nature of this virus, leading to infections similar to the severe acquired respiratory syndrome, increased the importance of early detection of COVID-19 to prevent the further spreading of this disease. The current gold standard is the reverse transcription–polymerase chain reaction (RT-PCR) to diagnose COVID-19 viral
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