A Review of Brain Tumor Image Classification Using Graph Convolutional Neural Networks
DOI:
https://doi.org/10.54097/40vm1f42Keywords:
Graph convolution network, Deep learning, Brain tumorAbstract
Brain tumor refers to a cluster of abnormally proliferating cells in the brain, usually formed by uncontrolled division of malignant cancer cells, and belongs to high-risk neurological disorders. At present, the specific pathogenic mechanism of brain tumors has not been fully elucidated in the medical community. But research has shown that early screening can effectively prevent tumor malignancy and significantly improve clinical cure rates. In recent years, breakthroughs in deep learning technology have driven the development of intelligent assisted diagnostic systems, providing medical personnel with more efficient and accurate early detection methods. Among them, Graph Convolutional Neural Network (GCN) has become an important tool in the field of brain tumor image classification due to its powerful global feature extraction ability, especially in analyzing the correlation of medical images. This article systematically reviews the theoretical evolution of Graph Convolutional Neural Networks (GCN) and summarizes the current research progress on GCN based classification models for brain tumor datasets. At the end of the article, the challenges and future development directions faced by this technology were further discussed.
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