An improved CapsNet applied to recognition of 3D vertebral images

摘要

Deep learning is currently widely applied in medical image processing and has achieved good results. However, recognizing vertebrae via image processing remains a challenging problem due to their complex spatial structures. CapsNet is a newly proposed network whose characteristics compensate for some shortcomings of traditional CNNs, and it has been shown to perform well on many tasks, including medical image recognition. In this paper, we applied a modified CapsNet to recognise 3D vertebral images by introducing an RNN module into CapsNet to further enhance its learning ability. This new network is called RNNinCaps, and it achieves the highest recognition performance on 3D vertebral images (the average accuracy of RNNinCaps exceeds the accuracy of the original CapsNet by 46.2% and that of a traditional CNN by 12.6%). RNNinCaps also performs better than several mainstream networks. RNNinCaps can promotes CapsNet’s application in the field of 3D medical image recognition.