Drawing-Aware Parkinson’s Disease Detection Through Hierarchical Deep Learning Models
Parkinson’s disease (PD) is a chronic neurological disorder that progresses slowly and shares symptoms with other diseases. Early detection and diagnosis are vital for appropriate treatment through medication and/or occupational therapy, ensuring patients can lead productive and healthy l...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10855391/ |
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| author | Ioannis Kansizoglou Konstantinos A. Tsintotas Daniel Bratanov Antonios Gasteratos |
| author_facet | Ioannis Kansizoglou Konstantinos A. Tsintotas Daniel Bratanov Antonios Gasteratos |
| author_sort | Ioannis Kansizoglou |
| collection | DOAJ |
| description | Parkinson’s disease (PD) is a chronic neurological disorder that progresses slowly and shares symptoms with other diseases. Early detection and diagnosis are vital for appropriate treatment through medication and/or occupational therapy, ensuring patients can lead productive and healthy lives. Key symptoms of PD include tremors, muscle rigidity, slow movement, and balance issues, along with psychiatric ones. Handwriting (HW) dynamics have been a prominent tool for detecting and assessing PD-associated symptoms. Still, many handcrafted feature extraction techniques suffer from low accuracy, which is rather than optimal for diagnosing such a serious condition. To that end, various machine learning (ML) and deep learning (DL) approaches have been explored for early detection. Meanwhile, concerning the latter, large models that introduce complex and difficult-to-understand architectures reduce the system’s recognition transparency and efficiency in terms of complexity and reliability. To tackle the above problem, an efficient hierarchical scheme based on simpler DL models is proposed for early PD detection. This way, we deliver a more transparent and efficient solution for PD detection from HW records. At the same time, we conclude that a careful implementation of each component of the introduced hierarchical pipeline enhances recognition rates. A rigorous 5-fold cross-validation strategy is adopted for evaluation, indicating our system’s robust behavior under different testing scenarios. By directly comparing it against a similar end-to-end classifier, the benefits of our technique are clearly illustrated during experiments. Finally, its performance is compared against several state-of-the-art ML- and DL-based PD detection methods, demonstrating the method’s supremacy. |
| format | Article |
| id | doaj-art-14e27934e7c6421292094993e906af82 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| spelling | doaj-art-14e27934e7c6421292094993e906af822025-02-06T00:00:33ZengIEEEIEEE Access2169-35362025-01-0113218802189010.1109/ACCESS.2025.353523210855391Drawing-Aware Parkinson’s Disease Detection Through Hierarchical Deep Learning ModelsIoannis Kansizoglou0https://orcid.org/0000-0003-2064-6442Konstantinos A. Tsintotas1https://orcid.org/0000-0002-1808-2601Daniel Bratanov2https://orcid.org/0000-0001-6787-0543Antonios Gasteratos3https://orcid.org/0000-0002-5421-0332Department of Production Engineering and Management, Democritus University of Thrace, Xanthi, GreeceDepartment of Information and Electronic Engineering, International Hellenic University, Alexander Campus, Thessaloniki, GreeceDepartment of Medical and Clinical-Diagnostic Activities, University of Ruse “A. Kanchev,”, Ruse, BulgariaDepartment of Production Engineering and Management, Democritus University of Thrace, Xanthi, GreeceParkinson’s disease (PD) is a chronic neurological disorder that progresses slowly and shares symptoms with other diseases. Early detection and diagnosis are vital for appropriate treatment through medication and/or occupational therapy, ensuring patients can lead productive and healthy lives. Key symptoms of PD include tremors, muscle rigidity, slow movement, and balance issues, along with psychiatric ones. Handwriting (HW) dynamics have been a prominent tool for detecting and assessing PD-associated symptoms. Still, many handcrafted feature extraction techniques suffer from low accuracy, which is rather than optimal for diagnosing such a serious condition. To that end, various machine learning (ML) and deep learning (DL) approaches have been explored for early detection. Meanwhile, concerning the latter, large models that introduce complex and difficult-to-understand architectures reduce the system’s recognition transparency and efficiency in terms of complexity and reliability. To tackle the above problem, an efficient hierarchical scheme based on simpler DL models is proposed for early PD detection. This way, we deliver a more transparent and efficient solution for PD detection from HW records. At the same time, we conclude that a careful implementation of each component of the introduced hierarchical pipeline enhances recognition rates. A rigorous 5-fold cross-validation strategy is adopted for evaluation, indicating our system’s robust behavior under different testing scenarios. By directly comparing it against a similar end-to-end classifier, the benefits of our technique are clearly illustrated during experiments. Finally, its performance is compared against several state-of-the-art ML- and DL-based PD detection methods, demonstrating the method’s supremacy.https://ieeexplore.ieee.org/document/10855391/Computer visiondeep learninghandwriting recognitionhierarchical systemsoccupational therapyParkinson’s disease |
| spellingShingle | Ioannis Kansizoglou Konstantinos A. Tsintotas Daniel Bratanov Antonios Gasteratos Drawing-Aware Parkinson’s Disease Detection Through Hierarchical Deep Learning Models IEEE Access Computer vision deep learning handwriting recognition hierarchical systems occupational therapy Parkinson’s disease |
| title | Drawing-Aware Parkinson’s Disease Detection Through Hierarchical Deep Learning Models |
| title_full | Drawing-Aware Parkinson’s Disease Detection Through Hierarchical Deep Learning Models |
| title_fullStr | Drawing-Aware Parkinson’s Disease Detection Through Hierarchical Deep Learning Models |
| title_full_unstemmed | Drawing-Aware Parkinson’s Disease Detection Through Hierarchical Deep Learning Models |
| title_short | Drawing-Aware Parkinson’s Disease Detection Through Hierarchical Deep Learning Models |
| title_sort | drawing aware parkinson x2019 s disease detection through hierarchical deep learning models |
| topic | Computer vision deep learning handwriting recognition hierarchical systems occupational therapy Parkinson’s disease |
| url | https://ieeexplore.ieee.org/document/10855391/ |
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