Multi-strategy fusion model and algorithm for lung nodule detection

Early detection of lung nodules can effectively improve the survival rate of patients. Automatic detection algorithm can effectively assist doctors in diagnosis. In the lung nodule detection task, in order to improve the detection accuracy and reduce the missed diagnosis rate, the lung nodule detection algorithm based on the recurrent residual attention gate U-Net (R2AGU-Net) was proposed. Firstly, recurrent residual convolution module and fusion attention gate mechanism were added in U-Net. The improved method can enhance the feature extraction performance, focus on the target nodule area and suppress irrelevant feature responses to obtain higher detection accuracy. Secondly, improved loss function can solve the problem of unbalanced image data of lung nodules to obtain higher detection sensitivity. Finally, the candidate nodules are classified by a three-dimensional convolutional neural network (3D CNN) to reduce false positive. Experimental verification on two datasets shows that the algorithm proposed in this paper improves the detection speed and sensitivity, achieves better performance than existing algorithms, and has better generalization capabilities.