Research of Children Dyslexia Classification Recognition based on Graph Convolutional Neural Networks

Developmental dyslexia is a common neurodevelopmental disorder that significantly affects children' normal learning and life. Early identification and intervention are crucial for patients. However, current models for classifying dyslexia fail to automatically extract features based on patient data and overlook the interrelationships between brain nodes in patients. Therefore, this paper proposes a graph convolutional neural network model that constructs a brain network from patients' fMRI data as an adjacency matrix, calculates node feature matrices, trains GCN models for classification, and achieves the diagnosis of dyslexia patients. Experimental results show that the model has an identification accuracy of 94%, precision of 95%, recall of 94%, and F1 score of 94%. This study provides a new approach for identifying and diagnosing dyslexia, which is beneficial for early intervention in dyslexia patients.