The invention relates to an
infantile autism classification method based on a double-
branch functional
topological graph neural network. The
infantile autism classification method can realize the classification of the
infantile autism by using
functional magnetic resonance imaging data. According to the provided
autism classification network, long-distance connection and short-distance connection are divided based on the shortest path between brain intervals, then an
exponential decay mask is introduced through a functional
topological graph Transform
branch to adjust attention weight and accurately extract long-distance dependency features, a graph isomorphic network in the other
branch is subjected to multiple neighborhood aggregation operations, short-distance dependency features are captured, and the short-distance dependency features are extracted. According to the method, multi-scale dependence of the
brain network is extracted in parallel through a double-
branch structure,
information redundancy is reduced by means of a topology
perception attention mechanism, and the adaptive ability of the model to the heterogeneous
brain network is improved by using the adaptive fusion module, so that multi-scale dependence of the heterogeneous
brain network is balanced in a self-adaptive manner. The classification accuracy is remarkably superior to that of an existing mainstream method, objective and efficient
technical support is provided for
autism diagnosis, and high
interpretability is achieved.