Name
Title | ||
|---|---|---|
Handwritten dynamics assessment through convolutional neural networks: An application to Parkinson's disease identification. |
Abstract | ||
|---|---|---|
•A deep learning approach to cope with Parkinson's disease (PD) diagnosis.•Handwritten dynamics to assess PD diagnosis.•Promising and accurate results.•An extensive experimental evaluation is conducted.•New insights about future research concerning automatic PD identification. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.artmed.2018.04.001 | Artificial Intelligence in Medicine |
Keywords | Field | DocType |
Parkinson's disease,Convolutional neural networks,Handwritten dynamics | Parkinson's disease,Gait,Degenerative Disorder,Convolutional neural network,Computer science,Raw data,Artificial intelligence,Motor system,Deep learning,Machine learning | Journal |
Volume | ISSN | Citations |
87 | 0933-3657 | 8 |
PageRank | References | Authors |
0.56 | 15 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Clayton Pereira | 1 | 82 | 8.52 |
| Danilo R. Pereira | 2 | 9 | 0.91 |
| Gustavo H. Rosa | 3 | 47 | 8.00 |
| Victor Hugo C. de Albuquerque | 4 | 914 | 83.30 |
| Silke A. T. Weber | 5 | 44 | 4.77 |
| Christian Hook | 6 | 16 | 1.72 |
| João P. Papa | 7 | 689 | 46.87 |