Ensemble Deep Learning with Majority Voting for Parkinson’s Diagnosis via Facial Images

Parkinson's disease (PD) is a progressive neurodegenerative disorder caused by the loss or damage of dopamine-producing brain cells. Early diagnosis is crucial, as timely treatment can enhance patients' quality of life and slow disease progression. Various methods, including brain imaging, neurological tests, handwriting and voice analysis, facial image assessment, and physical examination, are used for PD diagnosis. In this study, we propose a majority voting-based classification system for diagnosing PD using facial images. Our model integrates three different feature selection techniques—Correlation-Based Feature Selection (CFS), Pearson Correlation Coefficient (PCC), and Least Absolute Shrinkage and Selection Operator (LASSO)—within a Convolutional Neural Network (CNN) framework, a deep learning (DL) method. These three feature selection approaches contribute to the design of distinct views, which are then combined through majority voting to enhance classification accuracy. The dataset comprises facial images labeled by a neurology expert. Experimental results indicate that the proposed ensemble model outperforms individual weak classifiers, achieving higher classification accuracy. This model has the potential to assist medical professionals in diagnosing PD more efficiently and accurately, ultimately improving patient care and treatment outcomes.