<![CDATA[The limited availability of labeled medical images remains a major challenge in developing reliable deep learning-based disease detection systems. Conventional classification approaches generally require a large amount of abnormal data, whereas medical image annotation is time-consuming, costly, and highly dependent on radiological expertise. This study proposes an unsupervised anomaly detection model for chest X-ray images using a Variational Autoencoder (VAE), in which only normal images are utilized during the training process. The experiments were conducted on the COVID-19-Pneumonia-Normal Chest X-ray Images dataset, consisting of 5,228 images categorized into normal, pneumonia, and COVID-19 classes. The proposed framework includes image preprocessing, baseline VAE construction, sequential hyperparameter optimization, Beta-VAE implementation, and model evaluation using Accuracy, Precision, Recall, F1-Score, and the Area Under the Receiver Operating Characteristic Curve (AUROC). Experimental results demonstrate that the optimized model outperformed the baseline model, achieving an Accuracy of 97.50%, Precision of 96.32%, Recall of 100%, F1-Score of 98.12%, and an AUROC of 0.9999. These findings indicate that hyperparameter optimization and appropriate β coefficient selection improve latent representation learning, leading to more effective discrimination between normal and abnormal chest X-ray images. Therefore, the proposed approach has the potential to serve as an artificial intelligence-based early screening tool for chest radiograph analysis, particularly in scenarios where labeled medical data are limited.]]>
