The diagnosis of COVID-19 by means of transfer learning through X-ray images
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 de...
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| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
IEEE Computer Society
2021
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/42396/1/The%20diagnosis%20of%20COVID-19%20by%20means%20of%20transfer%20learning.pdf http://umpir.ump.edu.my/id/eprint/42396/2/The%20diagnosis%20of%20COVID-19%20by%20means%20of%20transfer%20learning%20through%20X-ray%20Images_ABS.pdf http://umpir.ump.edu.my/id/eprint/42396/ https://doi.org/10.23919/ICCAS52745.2021.9649899 |
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| Summary: | 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. |
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