Spectro-Image Analysis with Vision Graph Neural Networks and Contrastive Learning for Parkinson’s Disease Detection
This study presents a novel framework that integrates Vision Graph Neural Networks (ViGs) with supervised contrastive learning for enhanced spectro-temporal image analysis of speech signals in Parkinson’s disease (PD) detection. The approach introduces a frequency band decomposition strategy that tr...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Journal of Imaging |
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| Online Access: | https://www.mdpi.com/2313-433X/11/7/220 |
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| author | Nuwan Madusanka Hadi Sedigh Malekroodi H. M. K. K. M. B. Herath Chaminda Hewage Myunggi Yi Byeong-Il Lee |
| author_facet | Nuwan Madusanka Hadi Sedigh Malekroodi H. M. K. K. M. B. Herath Chaminda Hewage Myunggi Yi Byeong-Il Lee |
| author_sort | Nuwan Madusanka |
| collection | DOAJ |
| description | This study presents a novel framework that integrates Vision Graph Neural Networks (ViGs) with supervised contrastive learning for enhanced spectro-temporal image analysis of speech signals in Parkinson’s disease (PD) detection. The approach introduces a frequency band decomposition strategy that transforms raw audio into three complementary spectral representations, capturing distinct PD-specific characteristics across low-frequency (0–2 kHz), mid-frequency (2–6 kHz), and high-frequency (6 kHz+) bands. The framework processes mel multi-band spectro-temporal representations through a ViG architecture that models complex graph-based relationships between spectral and temporal components, trained using a supervised contrastive objective that learns discriminative representations distinguishing PD-affected from healthy speech patterns. Comprehensive experimental validation on multi-institutional datasets from Italy, Colombia, and Spain demonstrates that the proposed ViG-contrastive framework achieves superior classification performance, with the ViG-M-GELU architecture achieving 91.78% test accuracy. The integration of graph neural networks with contrastive learning enables effective learning from limited labeled data while capturing complex spectro-temporal relationships that traditional Convolution Neural Network (CNN) approaches miss, representing a promising direction for developing more accurate and clinically viable speech-based diagnostic tools for PD. |
| format | Article |
| id | doaj-art-6b07bc21298c46f08e2ddcbf44de193b |
| institution | Kabale University |
| issn | 2313-433X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj-art-6b07bc21298c46f08e2ddcbf44de193b2025-08-20T03:58:31ZengMDPI AGJournal of Imaging2313-433X2025-07-0111722010.3390/jimaging11070220Spectro-Image Analysis with Vision Graph Neural Networks and Contrastive Learning for Parkinson’s Disease DetectionNuwan Madusanka0Hadi Sedigh Malekroodi1H. M. K. K. M. B. Herath2Chaminda Hewage3Myunggi Yi4Byeong-Il Lee5Digital Healthcare Research Center, Pukyong National University, Busan 48513, Republic of KoreaIndustry 4.0 Convergence Bionics Engineering, Pukyoung National University, Busan 48513, Republic of KoreaIndustry 4.0 Convergence Bionics Engineering, Pukyoung National University, Busan 48513, Republic of KoreaCardiff School of Technologies, Cardiff Metropolitan University, Cardiff CF23 6PS, UKDigital Healthcare Research Center, Pukyong National University, Busan 48513, Republic of KoreaDigital Healthcare Research Center, Pukyong National University, Busan 48513, Republic of KoreaThis study presents a novel framework that integrates Vision Graph Neural Networks (ViGs) with supervised contrastive learning for enhanced spectro-temporal image analysis of speech signals in Parkinson’s disease (PD) detection. The approach introduces a frequency band decomposition strategy that transforms raw audio into three complementary spectral representations, capturing distinct PD-specific characteristics across low-frequency (0–2 kHz), mid-frequency (2–6 kHz), and high-frequency (6 kHz+) bands. The framework processes mel multi-band spectro-temporal representations through a ViG architecture that models complex graph-based relationships between spectral and temporal components, trained using a supervised contrastive objective that learns discriminative representations distinguishing PD-affected from healthy speech patterns. Comprehensive experimental validation on multi-institutional datasets from Italy, Colombia, and Spain demonstrates that the proposed ViG-contrastive framework achieves superior classification performance, with the ViG-M-GELU architecture achieving 91.78% test accuracy. The integration of graph neural networks with contrastive learning enables effective learning from limited labeled data while capturing complex spectro-temporal relationships that traditional Convolution Neural Network (CNN) approaches miss, representing a promising direction for developing more accurate and clinically viable speech-based diagnostic tools for PD.https://www.mdpi.com/2313-433X/11/7/220Vision Graph Neural Networkssupervised contrastive learningParkinson’s diseasespeech analysisfrequency band decompositionspectro-temporal analysis |
| spellingShingle | Nuwan Madusanka Hadi Sedigh Malekroodi H. M. K. K. M. B. Herath Chaminda Hewage Myunggi Yi Byeong-Il Lee Spectro-Image Analysis with Vision Graph Neural Networks and Contrastive Learning for Parkinson’s Disease Detection Journal of Imaging Vision Graph Neural Networks supervised contrastive learning Parkinson’s disease speech analysis frequency band decomposition spectro-temporal analysis |
| title | Spectro-Image Analysis with Vision Graph Neural Networks and Contrastive Learning for Parkinson’s Disease Detection |
| title_full | Spectro-Image Analysis with Vision Graph Neural Networks and Contrastive Learning for Parkinson’s Disease Detection |
| title_fullStr | Spectro-Image Analysis with Vision Graph Neural Networks and Contrastive Learning for Parkinson’s Disease Detection |
| title_full_unstemmed | Spectro-Image Analysis with Vision Graph Neural Networks and Contrastive Learning for Parkinson’s Disease Detection |
| title_short | Spectro-Image Analysis with Vision Graph Neural Networks and Contrastive Learning for Parkinson’s Disease Detection |
| title_sort | spectro image analysis with vision graph neural networks and contrastive learning for parkinson s disease detection |
| topic | Vision Graph Neural Networks supervised contrastive learning Parkinson’s disease speech analysis frequency band decomposition spectro-temporal analysis |
| url | https://www.mdpi.com/2313-433X/11/7/220 |
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