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Article Dans Une Revue Biomedical Signal Processing and Control Année : 2021

Evaluation of COVID-19 chest computed tomography: a texture analysis based on three-dimensional entropy

Andreia Gaudêncio
Pedro Vaz
  • Fonction : Auteur
Guillaume Mahé
  • Fonction : Auteur
  • PersonId : 963280
Mathieu Lederlin
João Cardoso
  • Fonction : Auteur

Résumé

Radiologists, and doctors in general, need relevant information for the quantification and characterization of pulmonary structures damaged by severe diseases, such as the Coronavirus disease 2019 (COVID-19). Texture-based analysis in scope of other pulmonary diseases has been used to screen, monitor, and provide valuable information for several kinds of diagnoses. To differentiate COVID-19 patients from healthy subjects and patients with other pulmonary diseases is crucial. Our goal is to quantify lung modifications in two pulmonary pathologies: COVID-19 and idiopathic pulmonary fibrosis (IPF). For this purpose, we propose the use of a three-dimensional multiscale fuzzy entropy (MFE3D) algorithm. The three groups tested (COVID-19 patients, IPF, and healthy subjects) were found to be statistically different for 9 scale factors (). A complexity index (CI) based on the sum of entropy values is used to classify healthy subjects and COVID-19 patients showing an accuracy of , a sensitivity of , and a specificity of . Moreover, 4 different machine-learning models were also used to classify the same COVID-19 dataset for comparison purposes.

Dates et versions

hal-03183076 , version 1 (26-03-2021)

Identifiants

Citer

Andreia Gaudêncio, Pedro Vaz, Mirvana Hilal, Guillaume Mahé, Mathieu Lederlin, et al.. Evaluation of COVID-19 chest computed tomography: a texture analysis based on three-dimensional entropy. Biomedical Signal Processing and Control, 2021, 68, pp.102582. ⟨10.1016/j.bspc.2021.102582⟩. ⟨hal-03183076⟩
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