The diagnosis of COVID-19 through X-ray images via transfer learning and fine-tuned dense layer on pipeline

Amiir Haamzah, Mohamed Ismail and Mohd Azraai, Mohd Razman and Ismail, Mohd Khairuddin and Muhammad Amirul, Abdullah and Rabiu Muazu, Musa and Anwar P. P., Abdul Majeed (2021) The diagnosis of COVID-19 through X-ray images via transfer learning and fine-tuned dense layer on pipeline. Mekatronika - Journal of Intelligent Manufacturing & Mechatronics, 3 (2). pp. 19-24. ISSN 2637-0883. (Published)

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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.

Item Type: Article
Uncontrolled Keywords: InceptionV3; Transfer learning; Hyperparameter; Dropout; OVAT
Subjects: R Medicine > RA Public aspects of medicine
T Technology > TJ Mechanical engineering and machinery
Faculty/Division: Institute of Postgraduate Studies
Faculty of Manufacturing and Mechatronic Engineering Technology
Depositing User: Mrs Norsaini Abdul Samat
Date Deposited: 09 May 2022 03:42
Last Modified: 09 May 2022 03:42
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