YOLO-CXR: A Novel Detection Network for Locating Multiple Small Lesions in Chest X-Ray Images

Chest X-ray is one of the most widely used methods for clinical diagnosis of chest diseases. In recent years, the development of deep learning technologies has driven progress in chest disease detection, but existing methods still face numerous challenges. Current research primarily focuses on detecting specific chest diseases. However, when chest X-ray images indicate multiple diseases, the diverse and complex characteristics of different disease types make it challenging to extract effective information. Additionally, the detection accuracy of small lesions remains low, which lessens the overall lesion detection rate. To address these issues, a novel network, named YOLO-CXR, is proposed in this paper for multiple disease detection, which is able to effectively locate multiple small lesions in chest X-ray images. Firstly, the proposed network enhances the YOLOv8s backbone by replacing the ordinary convolutional layers with RefConv layers to improve its feature extraction capabilities w.r.t. various diseases. Secondly, it utilizes a novel Efficient Channel and Local Attention (ECLA) mechanism to increase its sensitivity to the spatial location information of different lesions. Thirdly, to enhance its detection of small lesions, YOLO-CXR incorporates a dedicated small-lesion detection head and the Selective Feature Fusion (SFF) technique. Due to these improvements, the proposed network significantly enhances its detection of lesions at different scales and multiple small lesions in particular. Experiments conducted on the publicly available VinDr-CXR dataset demonstrate that YOLO-CXR achieves an [email protected] of 0.338, a mAP@[0.5:0.95:0.05]of 0.167, and recall of 0.365, outperforming all state-of-the-art networks considered.