TY - GEN
T1 - The Diagnosis of COVID-19 by Means of Transfer Learning through X-ray Images
AU - Mohamed Ismail, Amiir Haamzah
AU - Mohd Razman, Mohd Azraai
AU - Mohd Khairuddin, Ismail
AU - Musa, Rabiu Muazu
AU - Abdul Majeed, Anwar P.P.
N1 - Publisher Copyright:
© 2021 ICROS.
PY - 2021
Y1 - 2021
N2 - 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.
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
UR - http://www.scopus.com/inward/record.url?scp=85124183077&partnerID=8YFLogxK
U2 - 10.23919/ICCAS52745.2021.9649899
DO - 10.23919/ICCAS52745.2021.9649899
M3 - Conference Proceeding
AN - SCOPUS:85124183077
T3 - International Conference on Control, Automation and Systems
SP - 592
EP - 595
BT - 2021 21st International Conference on Control, Automation and Systems, ICCAS 2021
PB - IEEE Computer Society
T2 - 21st International Conference on Control, Automation and Systems, ICCAS 2021
Y2 - 12 October 2021 through 15 October 2021
ER -
