| Summary: | Pneumonia is the leading cause of death in children and one of the leading causes of death worldwide. Early detection of pneumonia in pediatric is needed so that children get proper treatment and avoid wider transmission. This study aims to detect pneumonia in children based on chest X-ray (CXR) images by comparing the Vision Transformer (ViT), InceptionV3, and Xception models. Vision Transformer (ViT) is a new transformer model that trains neural networks on image data. Meanwhile, the InceptionV3 and Xception models are transfer learning models in a convolutional neural network. This study uses a dataset called the Pediatric Chest X-ray Pneumonia, which contains two classes, namely normal and pneumonia. Vision Transformer achieved the best accuracy, precision, recall, and F1-measure values of 97.78, 97.46, 97.88, and 97.16. Furthermore, the second model, namely InceptionV3, obtained accuracy, precision, recall, and F1-measure values of 97.18, 96.06, 96.87, and 96.45. Finally, the Xception model obtained accuracy, precision, recall, and F1-measure values of 97.01, 95.85, 96.65, and 96.24. So, the conclusion of this research is that the Vision Transformer model can generate good performance for pediatric pneumonia detection based on CXR. © 2024 Author(s).
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