Facial Expression-based Parkinson's Disease Severity Diagnosis via Feature Fusion and Adaptive Class Balancing

TL;DR

This paper presents a novel method for diagnosing Parkinson's disease severity using facial expressions, combining feature fusion and adaptive class balancing to improve accuracy over existing binary or single-expression approaches.

Contribution

It introduces an attention-based feature fusion technique and an adaptive class balancing strategy specifically designed for PD severity diagnosis from facial expressions.

Findings

Enhanced diagnosis accuracy demonstrated in experiments

Effective handling of class imbalance across PD stages

Improved robustness over single-expression methods

Abstract

Parkinson's disease (PD) severity diagnosis is crucial for early detecting potential patients and adopting tailored interventions. Diagnosing PD based on facial expression is grounded in PD patients' "masked face" symptom and gains growing interest recently for its convenience and affordability. However, current facial expression-based approaches often rely on single type of expression which can lead to misdiagnosis, and ignore the class imbalance across different PD stages which degrades the prediction performance. Moreover, most existing methods focus on binary classification (i.e., PD / non-PD) rather than diagnosing the severity of PD. To address these issues, we propose a new facial expression-based method for PD severity diagnosis which integrates multiple facial expression features through attention-based feature fusion. Moreover, we mitigate the class imbalance problem via an adaptive class balancing strategy which dynamically adjusts the contribution of training samples based on their class distribution and classification difficulty. Experimental results demonstrate the promising performance of the proposed method for PD severity diagnosis, as well as the efficacy of attention-based feature fusion and adaptive class balancing.