Early Diagnosis of Parkinson’s Disease based on Handwritten Patterns using Deep Learning
| Autor: | Mohamed Aghzal, Asmaa Mourhir |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Parkinson's disease
business.industry Deep learning Pattern recognition Disease medicine.disease Convolutional neural network 03 medical and health sciences 0302 clinical medicine Histogram of oriented gradients Histogram Clinical diagnosis medicine 030212 general & internal medicine Artificial intelligence F1 score business 030217 neurology & neurosurgery |
| Zdroj: | 2020 Fourth International Conference On Intelligent Computing in Data Sciences (ICDS). |
| DOI: | 10.1109/icds50568.2020.9268738 |
| Popis: | Parkinson’s disease is a progressive neurodegenerative disorder. Although no cure has been found, medication can improve the symptoms. As such, early detection of the disease is essential to its treatment. However, most clinical methods used for diagnosis do not offer enough accuracy. In this work, a Histogram of Oriented Gradients is combined with a Convolutional Neural Network (CNN) to automatically detect Parkinson’s disease based on handwritten patterns. The model achieved an accuracy of 87% and an F1 score of 83.21%, which outperforms state of the art clinical diagnosis techniques (79.6% accuracy without follow up, 83% with follow up). |
| Databáze: | OpenAIRE |
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