Advanced Deep Neural Architectures for Parkinson’s Disease Prediction and Classification Systems

Introduction: Parkinson’s disease (PD) greatly decreases motor function in a progressive disease of the nervous system. Beginning accurate diagnoses establishes both on time action and more effective treatment of diseases. The traditional techniques for diagnosis depend upon individual examinations that could result in complications. The aim of this research is to examine potential of deep learning models—especially Convolutional Neural Networks (CNNs)—for PD identification and prediction integrating waveform and swirling drawing datasets as biomarkers. Methods: AlexNet, an already-trained deep neural system renowned for its excellent feature acquiring abilities, and a custom-made CNN model were evaluated. Augmentation approaches were employed in preliminary processing to boost picture variance and endurance. Standard performance indicators including preciseness, specificity, and sensitivity steered both models' training and evaluation. Results: AlexNet managed to retrieve convoluted spatial details and surpassed the modified CNN with preciseness by 100%. Although effectively functioning, the tailored CNN model accomplished a much less precise prediction of 93.2%. Superior accuracy and recall of AlexNet demonstrated the effectiveness in PD classifying more thoroughly. Conclusion: This research illustrates the significance of deep learning in the diagnosis of PD through exhibiting AlexNet's superior performance. These findings affirm the prospects for machine-driven, non-intrusive effectual PD examinations. Additional data sets and blended architectures should be examined in subsequent investigations to enhance model versatility