COVID-19 deep learning prediction model using publicly available radiologist-adjudicated chest X-ray images as training data: preliminary findings

Che Azemin, Mohd Zulfaezal and Hassan, Radhiana and Mohd Tamrin, Mohd Izzuddin and Md. Ali, Mohd. Adli (2020) COVID-19 deep learning prediction model using publicly available radiologist-adjudicated chest X-ray images as training data: preliminary findings. International Journal of Biomedical Imaging, 2020. pp. 1-7. ISSN 1687-4188 E-ISSN 1687-4196

	PDF (Article) - Published Version Restricted to Registered users only Download (3MB) | Request a copy
	PDF (Scopus) - Published Version Restricted to Registered users only Download (722kB) | Request a copy
	PDF (WoS) - Published Version Restricted to Registered users only Download (644kB) | Request a copy

Abstract

The key component in deep learning research is the availability of training data sets. With a limited number of publicly available COVID-19 chest X-ray images, the generalization and robustness of deep learning models to detect COVID-19 cases developed based on these images are questionable. We aimed to use thousands of readily available chest radiograph images with clinical findings associated with COVID-19 as a training data set, mutually exclusive from the images with confirmed COVID-19 cases, which will be used as the testing data set. We used a deep learning model based on the ResNet-101 convolutional neural network architecture, which was pretrained to recognize objects from a million of images and then retrained to detect abnormality in chest X-ray images. The performance of the model in terms of area under the receiver operating curve, sensitivity, specificity, and accuracy was 0.82, 77.3%, 71.8%, and 71.9%, respectively. The strength of this study lies in the use of labels that have a strong clinical association with COVID-19 cases and the use of mutually exclusive publicly available data for training, validation, and testing.

Item Type:	Article (Journal)
Additional Information:	5594/82304
Subjects:	R Medicine > RC Internal medicine > RC731 Specialties of Internal Medicine-Diseases of The Respiratory System T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices > TK7885 Computer engineering
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Allied Health Sciences Kulliyyah of Allied Health Sciences > Department of Optometry and Visual Science Kulliyyah of Information and Communication Technology Kulliyyah of Information and Communication Technology Kulliyyah of Information and Communication Technology > Department of Information System Kulliyyah of Information and Communication Technology > Department of Information System Kulliyyah of Medicine Kulliyyah of Medicine > Department of Radiology Kulliyyah of Science Kulliyyah of Science > Department of Physics
Depositing User:	Mohd Izzuddin Mohd Tamrin
Date Deposited:	24 Aug 2020 09:29
Last Modified:	18 Feb 2021 16:56
URI:	http://irep.iium.edu.my/id/eprint/82304

Actions (login required)

View Item