Pneumothorax Recognition Neural Network Based on Feature Fusion of Frontal and Lateral Chest X-Ray Images

Pneumothorax is a potentially life-threatening disease that requires urgent diagnosis and treatment. Clinically, a chest X-ray examination is the first choice for diagnosing pneumothorax. However, it is difficult to diagnose pneumothorax by only frontal chest X-ray imaging when the lesion area is only composed of a small amount of air. Therefore, we propose a pneumothorax diagnosis neural network based on feature fusion, where frontal and lateral X-ray information are fused. In this network, there are two inputs and three outputs. The two inputs are the frontal chest X-ray image and the lateral chest X-ray image. The three outputs are the classification results of the frontal chest X-ray image, the classification results of the lateral chest X-ray image, and the classification results integrating the characteristics of the fused frontal chest X-ray image and lateral chest X-ray image. Our algorithm considers the vanishing gradient problem in the pneumothorax recognition model and introduces the residual block to alleviate this problem. Because of the large number of channels in this model, we also utilize channel attention mechanisms to improve the model’s performance. Our comparative experiments show that neural network fusion of frontal and lateral chest image features can achieve higher accuracy than the single task model. Using only image-level annotation, our pneumothorax model can achieve high recognition accuracy.