Abstract: Based on radiomics analysis, an automatic diagnosis method of hippocampal sclerosis is proposed to find radiomics features to characterize hippocampal sclerosis lesions and improve the detection accuracy of hippocampal sclerosis. Combined with the wavelet transform and Laplacian of Gaussian filtering algorithm, the radiomics features in the region of hippocampus were extracted from several filtered images to obtain highly sensitive features and identify hippocampal sclerosis. First, pre-processing and filtering processes are applied to T1 image of tested samples, and radiomic features are extracted from the original image and the filtered image. Then, the student-T test and feature correlation analysis are used to reduce the dimension of the radiomic features. Finaly, hippocampal sclerosis detection models are built based on these features. The experimental results show that the hippocampal sclerosis detection model based on radiomics features can effectively assist the identification of hippocampal sclerosis lesions, and the radiomics features extracted from wavelet transform image have the best detection performance onthe automatic diagnosis model with a detection accuracy of 97.7% for the actual dataset.

Key words: radiomics, hippocampal sclerosis, machine learning, wavelet transform