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Abstract #2942

Automated hybrid approach to diagnose Parkinson's disease via deep learning and radiomics

Hongyi Chen1, Xueling Liu2, Yuxin Li1,2, Puyeh Wu3, and Daoying Geng1,2
1Academy for Engineering and Technology, Fudan University, Shanghai, China, 2Huashan Hospital, State Key Laboratory of Medical Neurobiology, Fudan University, Shanghai, China, 3GE Healthcare, Beijing, China

Synopsis

Keywords: Data Analysis, Parkinson's DiseaseIn this study, we constructed a hybrid machine learning model utilizing CNN and radiomics features based on NM-sensitive setMag images. The hybrid features improved the diagnostic performance in distinguishing PD patients from HC, as demonstrated in the SVM classifier, which demonstrated 95.7% accuracy, 92.9% sensitivity, and 100% specificity. The interpretability of the radiomics approach is better because radiomics features provide more interpretable biomarkers, while the CNN approach extracts deeper features from images. Furthermore, visualizing regions that influence classification decisions via saliency map can also enhance the interpretability of the CNN approach.

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Keywords