4.5 Article

A deep learning-based COVID-19 automatic diagnostic framework using chest X-ray images

期刊

BIOCYBERNETICS AND BIOMEDICAL ENGINEERING
卷 41, 期 1, 页码 239-254

出版社

ELSEVIER
DOI: 10.1016/j.bbe.2021.01.002

关键词

Chest X-ray radiographs; Coronavirus; Deep learning; Image processing; Pneumonia

资金

  1. Department of Science and Technology (DST) , Government of India
  2. EU Interreg, niCElife [CE1581]
  3. MVCR, Czech Rep. [VI04000039]

向作者/读者索取更多资源

A deep learning-based system is proposed for automatic detection and classification of COVID-19 using chest X-ray images, achieving a high accuracy rate in multi-class and binary classification. Infected patient's chest X-ray images reveal distinct opacities compared to healthy lungs, enabling a rapid and accurate diagnostic tool to assist healthcare professionals in managing the pandemic effectively.
The lethal novel coronavirus disease 2019 (COVID-19) pandemic is affecting the health of the global population severely, and a huge number of people may have to be screened in the future. There is a need for effective and reliable systems that perform automatic detection and mass screening of COVID-19 as a quick alternative diagnostic option to control its spread. A robust deep learning-based system is proposed to detect the COVID-19 using chest X-ray images. Infected patient's chest X-ray images reveal numerous opacities (denser, confluent, and more profuse) in comparison to healthy lungs images which are used by a deep learning algorithm to generate a model to facilitate an accurate diagnostics for multi-class classification (COVID vs. normal vs. bacterial pneumonia vs. viral pneumonia) and binary classification (COVID-19 vs. non-COVID). COVID-19 positive images have been used for training and model performance assessment from several hospitals of India and also from countries like Australia, Belgium, Canada, China, Egypt, Germany, Iran, Israel, Italy, Korea, Spain, Taiwan, USA, and Vietnam. The data were divided into training, validation and test sets. The average test accuracy of 97.11 +/- 2.71% was achieved for multi-class (COVID vs. normal vs. pneumonia) and 99.81% for binary classification (COVID-19 vs. non-COVID). The proposed model performs rapid disease detection in 0.137 s per image in a system equipped with a GPU and can reduce the workload of radiologists by classifying thousands of images on a single click to generate a probabilistic report in real-time. (c) 2021 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved.

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