Improving Deep Learning Parkinson’s Disease Detection Through Data Augmentation Training - Télécom Paris Accéder directement au contenu
Communication Dans Un Congrès Année : 2020

Improving Deep Learning Parkinson’s Disease Detection Through Data Augmentation Training

Résumé

Deep learning has been successfully applied to different classification applications where large data are available. However, the lack of data makes it more difficult to predict Parkinson’s disease (PD) with the deep models, which requires enough number of training data. Online handwriting dynamic signals can provide more detailed and complex information for PD detection task. In our previous work [1], two different deep models were studied for time series classification; the convolutional neural network (CNN) and the convolutional neural network- bidirectional long short term memory network (CNN-BLSTM). Different approaches were applied to encode pen-based signals into images for the CNN model while the raw time series are used directly with the CNN-BLSTM model. We have showed that both CNN model with spectrogram images as input and CNN-BLSTM model, improve the performance of time series classification applied for early PD stage detection. However, these approaches did not outperform classical support vector machine (SVM) classification applied on pre-engineered features. In this paper we investigate transfer learning and data augmentation approaches in order to train these models for PD detection on large-scale data. Various data augmentation methods for pen-based signals are proposed. Our experimental results show that the CNN-BLSTM model used with the combination of Jittering and Synthetic data augmentation methods provides promising results in the context of early PD detection, with accuracy reaching 97.62%. We have illustrated that deep architecture can surpass the models trained on pre-engineered features even though the available data is small.
Fichier non déposé

Dates et versions

hal-04267912 , version 1 (02-11-2023)

Identifiants

Citer

Catherine Taleb, Laurence Likforman-Sulem, Chafic Mokbel. Improving Deep Learning Parkinson’s Disease Detection Through Data Augmentation Training. MedPRAI 2019, 2019, Istamboul, Turkey. pp.79-93, ⟨10.1007/978-3-030-37548-5_7⟩. ⟨hal-04267912⟩
12 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More