ASCNet: Attention Mechanism and Self-Calibration Convolution Fusion Network for X-ray Femoral Fracture Classification

抄録

X-ray examinations are crucial for fracture diagnosis and treatment. However, some fractures do not present obvious imaging feature in early X-rays, which can result in misdiagnosis. Therefore, an ASCNet model is proposed in this study for X-ray femoral fracture classification. This model adopts the self-calibration convolution method to obtain more discriminative feature representation. This convolutional way can enable each spatial location to adaptively encode the context information of distant regions and make the model obtain some characteristic information hidden in X-ray images. Additionaly, the ASCNet model integrates the convolutional block attention module and coordinate attention module to capture different information from space and channels to fully obtain the apparent fracture features in X-ray images. Finally, the effectiveness of the proposed model is verified using the femoral fracture dataset. The final classification accuracy and AUC value of the ASCNet are 0.9286 and 0.9720, respectively. The experimental results demonstrate that the ASCNet model performs better than ResNet50 and SCNet50. Furthermore, the proposed model presents specific advantages in recognizing occult fractures in X-ray images.