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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
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Authors Affiliations
  1. 1. Urmia University, Computer Engineering Department, Urmia, Iran
  2. 2. Tehran University of Medical Sciences, Medical Physics and Biomedical Engineering Department, Tehran, Iran
  3. 3. Urmia University of Technology, Faculty of Information Technology and Computer Engineering, Urmia, Iran
  4. 4. Urmia University of Medical Sciences, Pharmacy Faculty, Urmia, Iran
  5. 5. University of Tehran, Faculty of New Sciences and Technologies, Tehran, Iran
  6. 6. Imam Khomeini Hospital Complex Tehran University of Medical Sciences, Department of Nuclear Medicine, Tehran, Iran

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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1. Covidense: Providing a Suitable Solution for Diagnosing Covid-19 Lung Infection Based on Deep Learning From Chest X-Ray Images of Patients, Frontiers in Biomedical Technologies (2021)
2. Application of Machine Learning in Diagnosis of Covid-19 Through X-Ray and Ct Images: A Scoping Review, Frontiers in Cardiovascular Medicine (2021)
3. Artificial Intelligence Approaches on X-Ray-Oriented Images Process for Early Detection of Covid-19, Journal of Medical Signals and Sensors (2022)
4. Medical Imaging and Computational Image Analysis in Covid-19 Diagnosis: A Review, Computers in Biology and Medicine (2021)
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