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Lightweight Method for the Rapid Diagnosis of Coronavirus Disease 2019 From Chest X-Ray Images Using Deep Learning Technique Publisher



Azar AS1 ; Ghafari A2 ; Najar MO3 ; Rikan SB1 ; Ghafari R4 ; Khamene MF5 ; Sheikhzadeh P6
Authors

Source: 2021 IEEE Nuclear Science Symposium and Medical Imaging Conference Record# NSS/MIC 2021 and 28th International Symposium on Room-Temperature Semiconductor Detectors# RTSD 2022 Published:2021


Abstract

A rapid screening method is required for screening coronavirus disease 2019 (COVID-19) patients. Therefore, we proposed a model based on DenseNet-201 to detect and differentiate COVID-19 patients from normal people and patients with other bacterial/viral cases of pneumonia using chest X-ray images. Our four-class model was found to have an accuracy of 91.01 ± 1.86 (mean ± standard deviation) and a sensitivity of 92.65 ± 1.28 using a five-fold cross-validation method. Moreover, it was a relatively lightweight and robust model with a simplified structure and fewer parameters, training, and testing epochs. As a supplementary diagnosis tool, physicians can detect COVID-19 faster using this model. © 2021 IEEE.
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