Impartially Validated Multiple Deep-Chain Models to Detect COVID-19 in Chest X-ray Using Latent Space Radiomics

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 diagno...

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Veröffentlicht in:Journal of clinical medicine Jg. 10; H. 14; S. 3100
Hauptverfasser: Yousefi, Bardia, Kawakita, Satoru, Amini, Arya, Akbari, Hamed, Advani, Shailesh M., Akhloufi, Moulay, Maldague, Xavier P. V., Ahadian, Samad
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland MDPI AG 14.07.2021
MDPI
Schlagworte:
Accuracy
Artificial intelligence
Biomarkers
Clinical medicine
Coronaviruses
COVID-19
Disease transmission
Health care
Machine learning
Medical imaging
Neural networks
Pandemics
Pneumonia
Radiomics
Viral infections
imaging biomarker
chest X-ray imaging
deep latent space radiomics
COVID-19 detection
deep convolutional autoencoder (ConvAE)
2D U-Net model
deep-learning features
ISSN:2077-0383, 2077-0383
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Zusammenfassung: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.
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ISSN:2077-0383
2077-0383
DOI:10.3390/jcm10143100