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A binary classification model of COVID-19 based on convolution neural network

Name: A binary classification model of COVID-19 based on convolution neural network
Keywords: Control and Optimization, Computer Networks and Communications, Hardware and Architecture, Control and Systems Engineering, COVID-19 CT-scan, Chest X-ray, Computer Science (miscellaneous), Convolutional neural networks, Deep learning, Electrical and Electronic Engineering

descriptionPublicationkeyboard_double_arrow_right Article 01 Jun 2023Institute of Advanced Engineering and ScienceBulletin of Electrical Engineering and Informatics, volume 12, pages 1,413-1,417 (issn: 2089-3191, eissn: 2302-9285,

Authors: Reham Sabah Saeed; Bushra Kadhim Oleiwi Chabor Alwawi;

doi: 10.11591/eei.v12i3.4832

A binary classification model of COVID-19 based on convolution neural network

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Abstract

The outbreak of the new coronavirus (COVID-19) had resulted in the creation of a disaster all over the world and it had become a highly acute and severe illness. The prevalence of this disease is increasing rapidly worldwide. The technology of deep learning (DL) became one of the hot topics in the computing context and it is widely implemented in a variety of the medical applications. Those techniques proved to be sufficient tools for the clinicians in automatic COVID-19 diagnosis. In the present study, a DL technology that is based on convolution neural networks (CNN) models had been suggested for the binary COVID-19 classification. In the initial step of the suggested model, COVID-19 data-set of chest X-ray (CXR) images have been obtained then preprocessed. Whereas in the second stage, a new CNN model has been built and trained for diagnosing COVID-19 data-set as (positive) infection or (negative) normal cases. The suggested architecture had a success in classifying COVID-19 with the training model accuracy that had reached 96.57% for the training data-set and 92.29% for validating data-set and could reach the target point with a minimal learning rate for training this model with promising results.

Keywords

Control and Optimization, Computer Networks and Communications, Hardware and Architecture, Control and Systems Engineering, COVID-19 CT-scan, Chest X-ray, Computer Science (miscellaneous), Convolutional neural networks, Deep learning, Electrical and Electronic Engineering, Instrumentation, Disease detection, Information Systems

25 references, page 1 of 3

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[7] A. F. Y. Althabhawee and B. K. O. C. Alwawi, “Fingerprint recognition based on collected images using deep learning technology,” IAES Int. J. Artif. Intell. IJ-AI, vol. 11, no. 1, Mar. 2022, doi: 10.11591/ijai.v11.i1.pp81-88. [OpenAIRE]

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[9] Y. Huang and N. Zhao, “Mental health burden for the public affected by the COVID-19 outbreak in China: Who will be the highrisk group?,” Psychol. Health Med., vol. 26, no. 1, pp. 23-34, Jan. 2021, doi: 10.1080/13548506.2020.1754438.

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	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	0
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Average
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Average
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Average

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