Prayogo, Kennard Alcander and Suryadibrata, Alethea and Young, Julio Cristian (2020) Classification of pneumonia from X- ray images using siamese convolutional network. TELKOMNIKA Telecommunication, Computing, Electronics and Control, 18 (3). ISSN 1693-6930
Full text not available from this repository.Abstract
Pneumonia is one of the highest global causes of deaths especially for children under 5 years old. This happened mainly because of the difficulties in identifying the cause of pneumonia. As a result, the treatment given may not be suitable for each pneumonia case. Recent studies have used deep learning approaches to obtain better classification within the cause of pneumonia. In this research, we used siamese convolutional network (SCN) to classify chest x-ray pneumonia image into 3 classes, namely normal conditions, bacterial pneumonia, and viral pneumonia. Siamese convolutional network is a neural network architecture that learns similarity knowledge between pairs of image inputs based on the differences between its features. One of the important benefits of classifying data with SCN is the availability of comparable images that can be used as a reference when determining class. Using SCN, our best model achieved 80.03% accuracy, 79.59% f1 score, and an improved result reasoning by providing the comparable images.
| Item Type: | Article |
|---|---|
| Subjects: | 500 Science and Mathematic > 510 Mathematics > 519 Probabilities and Applied Mathematics 600 Technology (Applied Sciences) > 610 Medicine and Health > 612 Human Physiology 600 Technology (Applied Sciences) > 610 Medicine and Health > 616 Diseases |
| Divisions: | Faculty of Engineering & Informatics > Informatics |
| Depositing User: | Administrator UMN Library |
| Date Deposited: | 05 Oct 2021 09:34 |
| Last Modified: | 05 Oct 2021 09:34 |
| URI: | https://kc.umn.ac.id/id/eprint/18537 |
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