Automated detection of COVID-19 cases using deep neural networks with X-ray images

The novel coronavirus 2019 (COVID-2019), which first appeared in Wuhan city of China in December 2019, spread rapidly around the world and became a pandemic. It has caused a devastating effect on both daily lives, public health, and the global economy. It is critical to detect the positive cases as...

Full description

Saved in:

Bibliographic Details
Published in:	Computers in biology and medicine Vol. 121; p. 103792
Main Authors:	Ozturk, Tulin, Talo, Muhammed, Yildirim, Eylul Azra, Baloglu, Ulas Baran, Yildirim, Ozal, Rajendra Acharya, U.
Format:	Journal Article
Language:	English
Published:	United States Elsevier Ltd 01-06-2020 Elsevier Limited
Subjects:	Artificial intelligence Artificial neural networks Betacoronavirus Chest X-ray images Classification Computational Biology Coronavirus (COVID-19) Coronavirus Infections - classification Coronavirus Infections - diagnosis Coronavirus Infections - diagnostic imaging Coronaviruses COVID-19 Databases, Factual Deep Learning Diagnosis, Computer-Assisted Diagnostic software Diagnostic systems Epidemics Female Global economy Humans Image detection Imaging techniques Lung - diagnostic imaging Male Medical imaging Middle Aged Model accuracy Neural networks Neural Networks, Computer Object recognition Pandemics Pandemics - classification Physicians Pneumonia - diagnosis Pneumonia - diagnostic imaging Pneumonia, Viral - classification Pneumonia, Viral - diagnosis Pneumonia, Viral - diagnostic imaging Public health Radiographic Image Interpretation, Computer-Assisted Radiology Radiology images SARS-CoV-2 Toolkits Viral diseases Viruses China Deep learning Coronavirus (COVID-19) Chest X-ray images Radiology images
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	The novel coronavirus 2019 (COVID-2019), which first appeared in Wuhan city of China in December 2019, spread rapidly around the world and became a pandemic. It has caused a devastating effect on both daily lives, public health, and the global economy. It is critical to detect the positive cases as early as possible so as to prevent the further spread of this epidemic and to quickly treat affected patients. The need for auxiliary diagnostic tools has increased as there are no accurate automated toolkits available. Recent findings obtained using radiology imaging techniques suggest that such images contain salient information about the COVID-19 virus. Application of advanced artificial intelligence (AI) techniques coupled with radiological imaging can be helpful for the accurate detection of this disease, and can also be assistive to overcome the problem of a lack of specialized physicians in remote villages. In this study, a new model for automatic COVID-19 detection using raw chest X-ray images is presented. The proposed model is developed to provide accurate diagnostics for binary classification (COVID vs. No-Findings) and multi-class classification (COVID vs. No-Findings vs. Pneumonia). Our model produced a classification accuracy of 98.08% for binary classes and 87.02% for multi-class cases. The DarkNet model was used in our study as a classifier for the you only look once (YOLO) real time object detection system. We implemented 17 convolutional layers and introduced different filtering on each layer. Our model (available at (https://github.com/muhammedtalo/COVID-19)) can be employed to assist radiologists in validating their initial screening, and can also be employed via cloud to immediately screen patients. [Display omitted] •Proposed deep model for early detection of COVID-19 cases using X-ray images.•Obtained accuracy of 98.08% and 87.02% for binary and multi-classes.•Proposed heatmaps can help the radiologists to locate the affected regions on chest X-rays.•DarkCovidNet model can assist the clinicians to make faster and accurate diagnosis.
Bibliography:	ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Undefined-1 ObjectType-Feature-3 content type line 23
ISSN:	0010-4825 1879-0534
DOI:	10.1016/j.compbiomed.2020.103792