Tumor Classification Based on Approximate Symmetry Using Dual-Branch Complementary Fusion Network

MRI technology is usually used to distinguish the grade of the tumor in the patient. Due to technical limitations, the classification of tumors (high-grade gliomas and metastases) on MRI images has become a problem for doctors. At present, the widely used neural network is gradually applied to the tumor classification of MRI images, which not only reduces the burden of human resources, but also shows good classification accuracy. Although different good experimental data are obtained under various neural networks, there is still a problem in using these neural networks for tumor classification: the semantic information expressed on the image by the deep features of the neural network is too scattered, and it is difficult to concentrate the lesion area. In this article, we propose a new strategy that combines the approximate symmetry properties of the MRI image with neural network, then uses a dual-branch network instead of the basic network for feature extraction, and adds complementary learning to the network, different features fusion and attention mechanism to enrich detailed information. Our method performs multiple comparison and ablation experiments on the dataset of glioma and metastasis, which proves that the proposed method is effective for tumor classification assisted by MRI.