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Department of Computer Science and Engineering, Indian Institute of Technology Ropar, Rupnagar, 140001, Punjab, India

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1 Papers

Mutually Exclusive Multi-Modal Approach for Parkinson’s Disease Classification

Topics: Feature Recognition and Extraction Methods; Applications of Machine Learning

Authors: Arunava Chaudhuri , Abhishek Sambyal and Deepti Bathula

Affiliation: Department of Computer Science and Engineering, Indian Institute of Technology Ropar, Rupnagar, 140001, Punjab, India

Digital Object Identifier: 10.5220/0012376100003657

Proceedings of BIOIMAGING 2024, ISBN: 978-989-758-688-0

Quick Abstract

Parkinson’s Disease (PD) is a progressive neurodegenerative disorder that affects the central nervous system and causes both motor and non-motor symptoms. While movement related symptoms are the most noticeable early signs, others like loss of smell can occur quite early and are easy to miss. This suggests that multi-modal assessment has significant potential in early diagnosis of PD. Multi-modal analysis allows for synergistic fusion of complementary information for improved prediction accuracy. However, acquiring all modalities for all subjects is not only expensive but also impractical in some cases. This work attempts to address the missing modality problem where the data is mutually exclusive. Specifically, we propose to leverage two distinct and unpaired datasets to improve the classification accuracy of PD. We propose a two-stage strategy that combines individual modality classifiers to train a multi-modality classifier using siamese network with Triplet Loss. Furthermore, we use a Max-Voting strategy applied to Mix-and-Match pairing of the unlabelled test sample of one modality with both positive and negative samples from the other modality for test-time inference. We conducted experiments using gait sensor data (PhysioNet) and clinical data (PPMI). Our experimental results demonstrate the efficacy of the proposed approach compared to the state-of-the-art methods using single modality analysis.