Prediction of Chest X-Ray Abnormalities using Ensemble Learning

Abstract

Abstract: Chest X-rays are a cornerstone of medical imaging, enabling rapid and cost-effective diagnosis of numerous chest pathologies. However, limitations in radiologist availability and long wait times can significantly delay diagnoses, potentially impacting patient outcomes. This study explores the potential of deep learning to address these challenges. Deep learning algorithms can analyse medical images and identify patterns associated with various diseases. In this study, Various deep learning models were trained and evaluated, including DenseNet121, EfficientNetB1, Xception, and an ensemble model combining EfficientNetB1 and Xception, using a random sample of 5,606 chest X-ray images from the National Institutes of Health (NIH) chest X-ray dataset. The ensemble model achieved the most promising results, demonstrating an accuracy of 72.66% and an AUC-ROC (Area Under the Receiver Operating Characteristic Curve) of 76.05%