For GEMASTIK XIII, Indonesia Annual Data Mining Competition by Ministry of Higher Education & Telkom-U
Teknologi Informasi dan Komunikasi untuk Indonesia Maju
Written by Yakuy 2, Universitas Gadjah Mada
- Ardacandra Subiantoro (18/427572/PA/18532)
- Arief Pujo Arianto (18/430253/PA/18766)
- Chrystian (18/430257/PA/18770)
The COVID-19 pandemic has many negative impacts on Indonesia and the rest of the world, so it is important to have a rigorous way to detect patients infected with COVID-19. X-Ray image of the patient's chest can help to detect whether the patient is infected with COVID-19 or not. We train Capsule Neural Network model to be able to classify an infected chest X-Ray with the COVID-19 virus. Models were trained with 6.310 Chest X-Ray images divided into three classes: Normal, Pneumonia, and Covid-19. The advantage of using CapsNet compared to traditional Convolutional Neural Network models : viewpoint invariance, less parameters, and good generalizations. We are able to train the model with results, validation accuracy of 0.9556 with test accuracy 0.9429. The model could be used to provide additional information to help detect COVID-19 from the patient's chest X-Ray.