Journal of Angiotherapy
Angiogenesis, Inflammation & Therapeutics | Online ISSN  2207-872X
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Contents Vol 8 (3)

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

Aruna Kokkula 1*, P. Chandra Sekhar 2, T. M. Praneeth Naidu 3

+ Author Affiliations

Journal of Angiotherapy 8 (3) 1-11 https://doi.org/10.25163/angiotherapy.839559

Submitted: 10 January 2024 Revised: 01 March 2024  Published: 04 March 2024 

Abstract

Background: The incidence of neurodegenerative diseases like Alzheimer's and Parkinson's is increasing among ageing populations in industrialized nations, highlighting the need for better and faster diagnostic tools for early detection. Advanced machine learning algorithms have emerged to assist in the categorization and preliminary risk evaluation of Parkinson's disease (PD) patients, leveraging publicly available data. This study aimed to enhance PD diagnosis using Magnetic Resonance Imaging (MRI) by examining deep learning architectures and evaluating their performance using various criteria. Methods: This study developed an accurate PD detection technique using MRI scans. A novel deep learning system was implemented, incorporating a Fully Connected (FC) layer and dropout, with a proposed VGG19 model that evolves through normalization and incrementing layers. These enhancements improve model normalization, ability, and task-specific flexibility. The training process utilized MRI scans from both healthy individuals and PD patients. Results: The network was tested on extensive MRI datasets for tasks including dementia grading, brain tumor classification, and disease classification, using 10-fold cross-validation. The Attention Feature Fusion VGG19 (AFF-VGG19) network achieved an accuracy of 0.984 in differentiating between three classes of brain tumors, 0.976 in distinguishing between Alzheimer's and Parkinson's diseases, and 0.977 in grading dementia cases. Conclusion: This research suggests that integrating attention modules, feature-fusion blocks, and baseline convolutional neural networks significantly enhances the performance of deep learning models in diagnosing neurodegenerative diseases using MRI scans.

Keywords: Deep learning, Parkinson's disease, VGG19, MRI images.

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