<![CDATA[Pulmonary edema is a lung disorder characterized by fluid accumulation in the alveolar and interstitial spaces, which disrupts gas exchange and reduces oxygen levels in the blood. Chest X-ray (CXR) imaging is commonly used for pulmonary edema assessment because it is fast and widely available; however, interpretation of CXR images heavily depends on radiologist expertise and may result in diagnostic variability, particularly in healthcare facilities with limited radiology resources. This study aims to develop an automated pulmonary edema detection system based on deep learning to support more consistent analysis of CXR images. The proposed method utilizes the YOLOv5n model as a lightweight object detection architecture due to its computational efficiency and suitability for resource-constrained environments. To improve detection performance and training stability, hyperparameters of the YOLOv5n model are optimized using the Grey Wolf Optimizer (GWO). The model is trained and evaluated using annotated CXR images, and its performance is assessed using precision, recall, mAP@0.5, and mAP@0.5–0.95 metrics. Experimental results show that the integration of GWO improves detection accuracy and model stability compared to the baseline configuration. The proposed framework demonstrates the potential to support pulmonary edema screening by providing efficient and consistent analysis of CXR images and can function as a decision-support tool for assisting medical personnel in early disease detection.]]>
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