SOCR-YOLO: Small Objects Detection Algorithm in Medical Images

Abstract

In the field of medical image analysis, object detection plays a crucial role by providing interpretable diagnostic information to healthcare professionals. Although current object detection models have achieved remarkable success in conventional images, their performance in detecting abnormalities in medical images has not been as satisfactory. This is primarily due to the complexity of anatomical structures in medical images, and the fact that some lesions may have subtle features, particularly in the case of early-stage, small-scale abnormalities. To address this challenge, we introduce SOCR-YOLO, a novel lesion detection model with online convolutional reparameterization based on channel shuffling. First, it employs the SOCR (Shuffled Channel with Online Convolutional Re-parameterization) module to establish a connection between feature concatenation and computational efficiency, aiming to extract more comprehensive information while reducing time consumption. Second, it incorporates the Bi-FPN structure to achieve multiscale feature fusion. Lastly, the loss function has been optimized to improve the model training process. We evaluated two datasets, chest x-ray (Vindr-CXR) and brain tumor (Br35H), provided by the Kaggle competition. Experimental results show that the proposed method has outperformed several state-of-the-art models, including YOLOv8, YOLO-NAS, and RT-DETR, in both speed and accuracy. Notably, in the context of chest x-ray anomaly detection, SOCR-YOLO exhibits a 1.8% enhancement in accuracy over YOLOv8 while simultaneously reducing floating-point operations by 26.3%. Additionally, a similar 1.8% improvement in accuracy is observed in the detection of brain tumors. The results indicate the superior ability of our model to detect multiscale variations and small lesions.