Abut, Serdar2024-12-242024-12-2420242757-9255https://doi.org/10.21605/cukurovaumfd.1514012https://search.trdizin.gov.tr/tr/yayin/detay/1250088https://hdl.handle.net/20.500.12604/4506Chronic Obstructive Pulmonary Disease (COPD) ranks high among the leading causes of death, particularly in middle- and low-income countries. Early diagnosis of COPD is challenging, with limited diagnostic methods currently available. In this study, a artificial intelligence model named COPD-GradeNet is proposed to predict COPD grades from radiographic images. However, the model has not yet been tested on a dataset. Obtaining a dataset including spirometric test results and chest X-ray images for COPD is a challenging process. Once the proposed model is tested on an appropriate dataset, its ability to predict COPD grades can be evaluated and implemented. This study may guide future research and clinical applications, emphasizing the potential of artificial intelligence-based approaches in the diagnosis of COPD.eninfo:eu-repo/semantics/openAccessDeep learningCOPDArtificial intelligenceTransfer learningMedical image processingArticle392325338125008810.21605/cukurovaumfd.1514012