Fit of the non-parametric empirical distribution to the variance-covariance matrix data: Class 0 (normal, blue dots), Class 1 (COVID-19, red dots), Class 2 (viral pneumonia, purple dots), and Class 3 (lung opacity, yellow dots).

Fit of the non-parametric empirical distribution to the variance-covariance matrix data: Class 0 (normal, blue dots), Class 1 (COVID-19, red dots), Class 2 (viral pneumonia, purple dots), and Class 3 (lung opacity, yellow dots).

<p>Fit of the non-parametric empirical distribution to the variance-covariance matrix data: Class 0 (normal, blue dots), Class 1 (COVID-19, red dots), Class 2 (viral pneumonia, purple dots), and Class 3 (lung opacity, yellow dots).</p>

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Bibliographic Details
Main Author:	Antonio Quintero-Rincón (21087716) (author)
Other Authors:	Ricardo Di-Pasquale (21087719) (author), Karina Quintero-Rodríguez (21087722) (author), Hadj Batatia (9606336) (author)
Published:	2025
Subjects:	Medicine Biotechnology Science Policy Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified proposed method implements open research field minimum covariance determinant image texture analysis developing automated tools conditional indices extracted conditional indices ), classic performance metrics true positive rate ray public dataset ray images based false negative rate false discovery rate positive predictive values image texture features experimental results demonstrating ray chest images work proposes using detect abnormal x singular value decomposition single parameter acts imbalanced chest x singular values chest x ray attenuation two features results show decomposition proportions tested using estimated using accuracy rate classify x without applying viral pneumonia total cost tissues affected tissue attenuation reducing misclassification parametric distribution paper presents paper introduces lung opacity collinearity diagnosis bandwidth yielded art methods &# 8220
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