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.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords