IMAGE SYNTHESIS USING GENERATIVE ADVERSARIAL NETWORK TO OVERCOME IMBALANCE PROBLEMS IN CHEST X-RAY IMAGE CLASSIFICATION CASE - Faris Muzakki, Muhammad
The imbalance of datasets in chest X-ray presents a significant challenge in building accurate and reliable pre-diagnosis models. Imbalance occurs when one label within the dataset has a much lower occurrence compared to other labels. Utilizing an imbalanced dataset for pre-diagnosis model construction can lead to underfitting and overfitting conditions. Although some studies have been conducted by adapting learning algorithms, such approaches do not address the issue of imbalanced data distribution. In this paper, we generate synthetis X-ray images using generative adversarial network algorithms to enhance the classification model for pneumonia infection cases. This study produces synthesis X-ray images with lower Fréchet Inception Distance score compared to conventional data augmentation and SMOTE. Additionally, the classification model with the addition