Modified VGG-19 Deep Learning Strategies for Parkinson's Disease Diagnosis - A Comprehensive Review and Novel Approach

Abstract

Parkinson's Disease (PD) poses a significant risk to clinical experts due to its rapid progression, adversely affecting individuals afflicted by the condition. This neurological disorder manifests through a spectrum of motor and non-motor symptoms, ranging from motor impairments like stiffness and bradykinesia to mental health issues and stress-related illnesses. Early and accurate diagnosis is pivotal for effective treatment, yet traditional diagnostic methods present challenges in detection and management. Leveraging Deep Learning (DL) techniques, particularly Convolutional Neural Networks (CNNs), holds promise for enhancing PD diagnosis. This study provides an extensive literature review of recent advancements in DL-based PD diagnosis, encompassing various approaches and developments. Specifically, the proposed modification to the VGG19 model, incorporating additional layers and regularization techniques, demonstrates remarkable performance in distinguishing PD patients from healthy individuals. Experimental results indicate a 98% accuracy rate, underscoring the model's potential as a reliable tool for early PD detection. Moreover, the study evaluates performance metrics, including accuracy, precision, recall, and F1-score, across different DL models, with the modified VGG19 model outperforming alternatives. The findings suggest that DL algorithms, particularly the proposed model, exhibit superior capability in identifying relevant cases and minimizing misclassifications. In conclusion, this research presents a novel DL approach for PD diagnosis using MRI images, offering substantial advancements in accuracy and efficiency. The model's ability to detect PD with high precision non-invasively underscores its significance in facilitating timely treatment and improving patient outcomes. Ultimately, early and accurate diagnosis facilitated by DL methodologies has the potential to revolutionize PD management, marking a significant stride towards enhancing patient care in neurology.