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Retraction Note: Refining Parkinson’s neurological disorder identification through deep transfer learning

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Retraction Crossref 1 open · 1 total · 0 disputed
DOI
10.1007/s00521-019-04069-0
Notice DOI
10.1007/s00521-024-09843-3
Event date
2024-04-25
Machine twin
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01One-hop citing occurrences

Retraction Open
Impact of canny edge detection preprocessing on performance of machine learning models for Parkinson's disease classification

ref [32] · 2609.07408 · notice #10954 · dispute

Raw extraction · citation context

Automated restricted Boltzmann machine classifier for early diagnosis of Parkinson's disease using digitized spiral drawings. J. Ambient Intell. Humaniz. Comput. 14, 175-189. https://doi.org/10.1007/s12652-022-04361-3 (2023). 32. Naseer, A. et al. Refining Parkinson's neurological disorder identification through deep transfer learning. Neural Comput. Appl. 32, 839-854. https://doi.org/10.1007/s00521-019-04069-0 (2020). 33. Ferdib-Al-Islam & Akter, L. Early Identification of Parkinson's Disease from Hand-drawn Images using Histogram of Oriented Gradients and Machine Learning Techniques. In,. Emerging Technology in Computing. Communication and Electronics (ETCCE) 1-6, 2020. https://doi.org/10.1109/ETCCE51779.2020.9350870 (IEEE, Bangladesh) (2020). 34. Loh, H.

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Automated restricted Boltzmann machine classifier for early diagnosis of Parkinson's disease using digitized spiral drawings. J. Ambient Intell. Humaniz. Comput. 14, 175-189. https://doi.org/10.1007/s12652-022-04361-3 (2023). 32. Naseer, A. et al. Refining Parkinson's neurological disorder identification through deep transfer learning. Neural Comput. Appl. 32, 839-854. https://doi.org/10.1007/s00521-019-04069-0 (2020). 33. Ferdib-Al-Islam & Akter, L. Early Identification of Parkinson's Disease from Hand-drawn Images using Histogram of Oriented Gradients and Machine Learning Techniques. In,. Emerging Technology in Computing. Communication and Electronics (ETCCE) 1-6, 2020. https://doi.org/10.1109/ETCCE51779.2020.9350870 (IEEE, Bangladesh) (2020). 34. Loh, H

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