The Diagnosis of COVID-19 through X-ray Images via Transfer Learning and Fine-Tuned Dense Layer on Pipeline

Authors

  • Amiir Haamzah Mohamed Ismail Innovative Manufacturing, Mechatronics and Sports Laboratory, Faculty of Manufacturing and Mechatronics Engineering Technology, Universiti Malaysia Pahang, 26600 Pahang, Malaysia.
  • Mohd Azraai Mohd Razman Innovative Manufacturing, Mechatronics and Sports Laboratory, Faculty of Manufacturing and Mechatronics Engineering Technology, Universiti Malaysia Pahang, 26600 Pahang, Malaysia.
  • Ismail Mohd Khairuddin Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang, 26600 Pekan Pahang, Malaysia.
  • Muhammad Amirul Abdullah Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang, 26600 Pekan Pahang, Malaysia.
  • Rabiu Muazu Musa Centre for Fundamental and Continuing Education, Department of Credited Co-curriculum, Universiti Malaysia Terengganu, Terengganu. Malaysia
  • Anwar P. P. Abdul Majeed Innovative Manufacturing, Mechatronics and Sports Laboratory, Faculty of Manufacturing and Mechatronics Engineering Technology, Universiti Malaysia Pahang, 26600 Pahang, Malaysia.

DOI:

https://doi.org/10.15282/mekatronika.v3i2.7161

Keywords:

InceptionV3, Transfer learning, Hyperparameter, Dropout, OVAT

Abstract

X-ray is used in medical treatment as a method to diagnose the human body internally from diseases. Nevertheless, the development in machine learning technologies for pattern recognition have allowed machine learning 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. Next, 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. InceptionV3 which is one of the common neural network is used for feature extraction from chest X-ray images. Subsequently, 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.

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Published

2021-07-29

How to Cite

[1]
A. H. Mohamed Ismail, M. A. Mohd Razman, I. Mohd Khairuddin, M. A. Abdullah, R. Muazu Musa, and A. P. P. Abdul Majeed, “The Diagnosis of COVID-19 through X-ray Images via Transfer Learning and Fine-Tuned Dense Layer on Pipeline”, MEKATRONIKA, vol. 3, no. 2, pp. 19–24, Jul. 2021.

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Original Article

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