AI-Powered Parkinson’s Detection: A Deep Learning Breakthrough for Early Diagnosis

This study evaluates the performance of VGG19 and VGG16 deep learning models for diagnosing Parkinson's disease (PD) using biomedical data. The objective was to assess their diagnostic accuracy and suitability for clinical applications. Both models were trained and tested on a dataset, with VGG19 achieving a test accuracy of 88.51% and an F1 score of 89.44%, while VGG16 recorded 86.49% accuracy and an F1 score of 87.95%. VGG19 slightly outperformed VGG16 in classifying PD-positive cases, with both models showing minimal overfitting and strong generalization on unseen data. Confusion matrix analysis confirmed balanced precision and recall, though potential class imbalances in the dataset require further investigation. These findings highlight the efficacy of VGG19 and VGG16 for early PD detection, emphasizing their potential to support timely interventions. Future improvements could include data augmentation, transfer learning, or threshold tuning to enhance diagnostic specificity. While promising, validation on diverse datasets is essential before clinical adoption.