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This invention belongs to the technical field of image classification and discloses a medical image classification method based on Lie group kernel learning. The method includes acquiring low-level image features and representing them as a Lie group matrix; obtaining the model parameters of an SVM classifier and the pivot point for each class through a training image set; and selecting either an SVM or KNN classifier to classify the images based on the geodesic distance between the class pivot point and each image to be classified, calculated using the Lie group kernel function on the Lie group manifold. This invention outperforms traditional image classification methods in terms of classification accuracy. Compared to artificial neural network methods, it has less dependence on training data and processes, and exhibits stronger generalization and deployability.