Abstract: Artificial Intelligence has significantly advanced and is proficient in image classification. Even though the COVID-19 pandemic has ended, the virus is now considered to have entered an endemic phase. Historically, COVID-19 detection has predominantly depended on a single technology known as the polymerase chain reaction (PCR). The academic community is keen radiograph data to forecast COVID-19 because of its prospective advantages. The proposed methodology aims to improve dataset quality by utilizing artificially generated images produced by StyleGAN. The ratio of 59:41 was used to combine the synthetic datasets with the real ones. The combination of the StyleGAN framework, the VGG19, and CatBoost Gradient Boosted Trees is to improve prediction accuracy. Accurate and precise measurements significantly impact the evaluation of a model's performance. The assessment resulted in 98.67% accurate and 97.21% precise. In the future, we may enhance the diversity and quality of the collection by integrating other datasets from different sources with the Chest X-ray dataset.

Andi Besse Firdausiah Mansur, “Disease-Aware Chest X-Ray Style GAN Image Generation and CatBoost Gradient Boosted Trees” International Journal of Advanced Computer Science and Applications(IJACSA), 15(3), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0150342

@article{Mansur2024,
title = {Disease-Aware Chest X-Ray Style GAN Image Generation and CatBoost Gradient Boosted Trees},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150342},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150342},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {3},
author = {Andi Besse Firdausiah Mansur}
}