Pneumonia X‐ray detection with anchor‐free detection framework and data augmentation

Abstract

AbstractPneumonia has become one of the main causes of human death. However, it is a tall order to efficiently and accurately diagnose pneumonia for clinicians. Therefore, A novel method based on anchor‐free detection framework is proposed to automatically locate lung opacities on chest radiographs in this study. We conducted extensive sets of experiments on the dataset of the Radiological Society of North America (RSNA) pneumonia detection challenge from the Kaggle competition. The results show superior performances for our method compared with previous studies. The best method achieved 52.9% in average precision (AP) and 97.5% in average recall (AR). For better interpretability of the results, visualization techniques are applied to provide visual explanations for our method. The visualization of these randomly selected samples shows that the method has excellent performance for lung opacity detection. Our method achieves better discriminative results and is suitable for the pneumonia diagnosis.