| Full text | |
| Author(s): |
Pereira, Clayton R.
[1]
;
Pereira, Danilo R.
[2]
;
Rosa, Gustavo H.
[3]
;
Albuquerque, Victor H. C.
[4]
;
Weber, Silke A. T.
[5]
;
Hook, Christian
[6]
;
Papa, Joao P.
[3]
Total Authors: 7
|
| Affiliation: | [1] UFSCAR Fed Univ Sao Carlos, Dept Comp, Sao Carlos, SP - Brazil
[2] UNOESTE Univ Western Sao Paulo, Presidente Prudente - Brazil
[3] UNESP Sao Paulo State Univ, Sch Sci, Bauru - Brazil
[4] UNIFOR Grad Program Appl Informat, Fortaleza, Ceara - Brazil
[5] UNESP Sao Paulo State Univ, Botucatu Med Sch, Botucatu, SP - Brazil
[6] OTH, Regensburg - Germany
Total Affiliations: 6
|
| Document type: | Journal article |
| Source: | ARTIFICIAL INTELLIGENCE IN MEDICINE; v. 87, p. 67-77, MAY 2018. |
| Web of Science Citations: | 15 |
| Abstract | |
Background and objective: Parkinson's disease (PD) is considered a degenerative disorder that affects the motor system, which may cause tremors, micrography, and the freezing of gait. Although PD is related to the lack of dopamine, the triggering process of its development is not fully understood yet. Methods: In this work, we introduce convolutional neural networks to learn features from images produced by handwritten dynamics, which capture different information during the individual's assessment. Additionally, we make available a dataset composed of images and signal-based data to foster the research related to computer-aided PD diagnosis. Results: The proposed approach was compared against raw data and texture-based descriptors, showing suitable results, mainly in the context of early stage detection, with results nearly to 95%. Conclusions: The analysis of handwritten dynamics using deep learning techniques showed to be useful for automatic Parkinson's disease identification, as well as it can outperform handcrafted features. (C) 2018 Elsevier B.V. All rights reserved. (AU) | |
| FAPESP's process: | 14/16250-9 - On the parameter optimization in machine learning techniques: advances and paradigms |
| Grantee: | João Paulo Papa |
| Support type: | Regular Research Grants |
| FAPESP's process: | 15/25739-4 - On the Study of Semantics in Deep Learning Models |
| Grantee: | Gustavo Henrique de Rosa |
| Support type: | Scholarships in Brazil - Master |
| FAPESP's process: | 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry |
| Grantee: | José Alberto Cuminato |
| Support type: | Research Grants - Research, Innovation and Dissemination Centers - RIDC |
| FAPESP's process: | 16/19403-6 - Energy-based learning models and their applications |
| Grantee: | João Paulo Papa |
| Support type: | Regular Research Grants |
| FAPESP's process: | 14/12236-1 - AnImaLS: Annotation of Images in Large Scale: what can machines and specialists learn from interaction? |
| Grantee: | Alexandre Xavier Falcão |
| Support type: | Research Projects - Thematic Grants |
| FAPESP's process: | 10/15566-1 - Evaluation of mathematic modell for study of fine motor function in individuals with Parkinson's disease using a multi-sensor biometric smart pen, BiSP |
| Grantee: | Silke Anna Theresa Weber |
| Support type: | Regular Research Grants |