The Diagnosis of COVID-19 by Means of Transfer Learning through X-ray Images

Amiir Haamzah Mohamed Ismail; Mohd Azraai Mohd Razman; Ismail Mohd Khairuddin; Rabiu Muazu Musa; Anwar P.P. Abdul Majeed

doi:10.23919/ICCAS52745.2021.9649899

The Diagnosis of COVID-19 by Means of Transfer Learning through X-ray Images

Amiir Haamzah Mohamed Ismail
, Mohd Azraai Mohd Razman
, Ismail Mohd Khairuddin
, Rabiu Muazu Musa
, Anwar P.P. Abdul Majeed^*

^*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

1 Citation (Scopus)

Abstract

Radiography is used in medical treatment as a method to diagnose the internal organs of the human body from diseases. However, the advancement in machine learning technologies have paved way to new possibilities of diagnosing diseases from chest X-ray images. One such diseases that are able to be detected by using X-ray is the COVID-19 coronavirus. This research investigates the diagnosis of COVID-19 through X-ray images by using transfer learning and fine-tuning of the fully connected layer. Hyperparameters such as dropout, p, number of neurons, and activation functions are investigated on which combinations of these hyperparameters will yield the highest classification accuracy model. VGG19 learning model created by the Visual Geometry Group is used for extraction of features from the patient's chest X-ray images. To evaluate the combination of various pipelines, the loss and accuracy graphs are used to find the pipeline which performs the best in classification task. The findings in this research will open new possibilities in screening method for COVID-19.

Original language	English
Title of host publication	2021 21st International Conference on Control, Automation and Systems, ICCAS 2021
Publisher	IEEE Computer Society
Pages	592-595
Number of pages	4
ISBN (Electronic)	9788993215212
DOIs	https://doi.org/10.23919/ICCAS52745.2021.9649899
Publication status	Published - 2021
Externally published	Yes
Event	21st International Conference on Control, Automation and Systems, ICCAS 2021 - Jeju, Korea, Republic of Duration: 12 Oct 2021 → 15 Oct 2021

Publication series

Name	International Conference on Control, Automation and Systems
Volume	2021-October
ISSN (Print)	1598-7833

Conference

Conference	21st International Conference on Control, Automation and Systems, ICCAS 2021
Country/Territory	Korea, Republic of
City	Jeju
Period	12/10/21 → 15/10/21

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

fully connected layer
hyperparameter
optimization
Transfer learning
VGG19

Access to Document

10.23919/ICCAS52745.2021.9649899

Cite this

Mohamed Ismail, A. H., Mohd Razman, M. A., Mohd Khairuddin, I., Musa, R. M., & Abdul Majeed, A. P. P. (2021). The Diagnosis of COVID-19 by Means of Transfer Learning through X-ray Images. In 2021 21st International Conference on Control, Automation and Systems, ICCAS 2021 (pp. 592-595). (International Conference on Control, Automation and Systems; Vol. 2021-October). IEEE Computer Society. https://doi.org/10.23919/ICCAS52745.2021.9649899

@inproceedings{262699f6028440338bde6ce2ab4198ac,

title = "The Diagnosis of COVID-19 by Means of Transfer Learning through X-ray Images",

abstract = "Radiography is used in medical treatment as a method to diagnose the internal organs of the human body from diseases. However, the advancement in machine learning technologies have paved way to new possibilities of diagnosing diseases from chest X-ray images. One such diseases that are able to be detected by using X-ray is the COVID-19 coronavirus. This research investigates the diagnosis of COVID-19 through X-ray images by using transfer learning and fine-tuning of the fully connected layer. Hyperparameters such as dropout, p, number of neurons, and activation functions are investigated on which combinations of these hyperparameters will yield the highest classification accuracy model. VGG19 learning model created by the Visual Geometry Group is used for extraction of features from the patient's chest X-ray images. To evaluate the combination of various pipelines, the loss and accuracy graphs are used to find the pipeline which performs the best in classification task. The findings in this research will open new possibilities in screening method for COVID-19.",

keywords = "fully connected layer, hyperparameter, optimization, Transfer learning, VGG19",

author = "\{Mohamed Ismail\}, \{Amiir Haamzah\} and \{Mohd Razman\}, \{Mohd Azraai\} and \{Mohd Khairuddin\}, Ismail and Musa, \{Rabiu Muazu\} and \{Abdul Majeed\}, \{Anwar P.P.\}",

note = "Publisher Copyright: {\textcopyright} 2021 ICROS.; 21st International Conference on Control, Automation and Systems, ICCAS 2021 ; Conference date: 12-10-2021 Through 15-10-2021",

year = "2021",

doi = "10.23919/ICCAS52745.2021.9649899",

language = "English",

series = "International Conference on Control, Automation and Systems",

publisher = "IEEE Computer Society",

pages = "592--595",

booktitle = "2021 21st International Conference on Control, Automation and Systems, ICCAS 2021",

}

Mohamed Ismail, AH, Mohd Razman, MA, Mohd Khairuddin, I, Musa, RM & Abdul Majeed, APP 2021, The Diagnosis of COVID-19 by Means of Transfer Learning through X-ray Images. in 2021 21st International Conference on Control, Automation and Systems, ICCAS 2021. International Conference on Control, Automation and Systems, vol. 2021-October, IEEE Computer Society, pp. 592-595, 21st International Conference on Control, Automation and Systems, ICCAS 2021, Jeju, Korea, Republic of, 12/10/21. https://doi.org/10.23919/ICCAS52745.2021.9649899

The Diagnosis of COVID-19 by Means of Transfer Learning through X-ray Images. / Mohamed Ismail, Amiir Haamzah; Mohd Razman, Mohd Azraai; Mohd Khairuddin, Ismail et al.
2021 21st International Conference on Control, Automation and Systems, ICCAS 2021. IEEE Computer Society, 2021. p. 592-595 (International Conference on Control, Automation and Systems; Vol. 2021-October).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

TY - GEN

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AU - Mohamed Ismail, Amiir Haamzah

AU - Mohd Razman, Mohd Azraai

AU - Mohd Khairuddin, Ismail

AU - Musa, Rabiu Muazu

AU - Abdul Majeed, Anwar P.P.

PY - 2021

Y1 - 2021

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AB - Radiography is used in medical treatment as a method to diagnose the internal organs of the human body from diseases. However, the advancement in machine learning technologies have paved way to new possibilities of diagnosing diseases from chest X-ray images. One such diseases that are able to be detected by using X-ray is the COVID-19 coronavirus. This research investigates the diagnosis of COVID-19 through X-ray images by using transfer learning and fine-tuning of the fully connected layer. Hyperparameters such as dropout, p, number of neurons, and activation functions are investigated on which combinations of these hyperparameters will yield the highest classification accuracy model. VGG19 learning model created by the Visual Geometry Group is used for extraction of features from the patient's chest X-ray images. To evaluate the combination of various pipelines, the loss and accuracy graphs are used to find the pipeline which performs the best in classification task. The findings in this research will open new possibilities in screening method for COVID-19.

KW - fully connected layer

KW - hyperparameter

KW - optimization

KW - Transfer learning

KW - VGG19

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Y2 - 12 October 2021 through 15 October 2021

ER -

Mohamed Ismail AH, Mohd Razman MA, Mohd Khairuddin I, Musa RM, Abdul Majeed APP. The Diagnosis of COVID-19 by Means of Transfer Learning through X-ray Images. In 2021 21st International Conference on Control, Automation and Systems, ICCAS 2021. IEEE Computer Society. 2021. p. 592-595. (International Conference on Control, Automation and Systems). doi: 10.23919/ICCAS52745.2021.9649899

The Diagnosis of COVID-19 by Means of Transfer Learning through X-ray Images

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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