Chest X-ray Image Classification for COVID-19 diagnoses
Background: Radiologists used chest radiographs to detect coronavirus disease 2019 (COVID-19) in patients and determine the severity levels. The COVID-19 cases were grouped into five classes, each receiving different treatments. An intelligent system is needed to advance the detection and identify v...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Universitas Airlangga
2022-10-01
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| Series: | Journal of Information Systems Engineering and Business Intelligence |
| Online Access: | https://e-journal.unair.ac.id/JISEBI/article/view/37958 |
| Summary: | Background: Radiologists used chest radiographs to detect coronavirus disease 2019 (COVID-19) in patients and determine the severity levels. The COVID-19 cases were grouped into five classes, each receiving different treatments. An intelligent system is needed to advance the detection and identify vector features of X-ray images with a quality that is too poor to be read by radiologists. Deep learning is an intelligent system that can be used in this case.
Objective: The current study compares the classification and accuracy of detection methods with two, three dan five classes.
Methods: Deep learning can classify visual geometry group VGG 19 architectures with 1000 classes. The classification of the five classes' convolutional neural network (CNN) underwent model validation with a confusion matrix to produce accuracy and class values. The system could then diagnose patients’ examinations by radiology specialists.
Results: The results of the five-class method showed 98% accuracy, the three-class method showed 99.99%, and the two-class showed 99.99%.
Conclusion: It can be concluded that using the VGG 19 model is effective. This system can classify and diagnose viruses in patients to assist radiologists by reading the images.
Keywords: COVID-19, CNN, Classification, Deep Learning |
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| ISSN: | 2598-6333 2443-2555 |