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LSTM-CNN: An efficient diagnostic network for Parkinson's disease utilizing dynamic handwriting analysis
Conclusions: We present a series of experiments with extensive analysis, which systematically demonstrate the effectiveness and efficiency of the proposed hybrid neural network in extracting distinctive handwriting patterns for precise diagnosis of Parkinson's disease.
A Light-weight CNN Model for Efficient Parkinson's Disease Diagnostics
In recent years, deep learning methods have achieved great success in various fields due to their strong performance in practical applications.
CNN Based Analysis of the Luria’s Alternating Series Test for Parkinson’s Disease Diagnostics
Luria's alternating series tests belong to the family of fine-motor drawing tests and been used in neurology and psychiatry for nearly a century.
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