ParkEnNET: a majority voting-based ensemble transfer learning framework for early Parkinson's disease detection.

Abstract

Parkinson's Disease (PD) is a rapidly progressing neurodegenerative disorder that often presents neuropsychiatric symptoms, affecting millions globally, particularly within aging populations. Addressing the urgent need for early and accurate diagnosis, this study introduces ParkEnNET, a Majority Voting-Based Ensemble Transfer Learning Framework for early PD detection. Traditional deep learning models, although powerful, require large labeled datasets, extensive computational resources, and are prone to overfitting when applied to small, noisy medical datasets. To overcome these limitations, ParkEnNET leverages transfer learning, utilizing pretrained deep learning models to efficiently extract relevant features from limited MRI data. By integrating the strengths of multiple models through a majority voting ensemble strategy, ParkEnNET effectively handles challenges such as data variability, class imbalance, and imaging noise. The framework was validated both through internal testing and on an independent clinical dataset collected from Superspeciality Hospital Jammu, ensuring real-world generalizability. Experimental results demonstrated that ParkEnNET achieved a diagnostic accuracy of 98.23%, with a precision of 100.0%, recall of 95.24%, and an F1-score of 97.44%, outperforming all individual models including VGGNet, ResNet-50, and EfficientNet. These outcomes establish ParkEnNET as a promising diagnostic framework with strong performance on limited datasets, offering significant potential to enhance early clinical detection and timely intervention for Parkinson's Disease.