Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/116094
Título: Ensemble of convolutional neural networks for Parkinson’s disease diagnosis from offline handwriting
Autores/as: Gazda, Matej
Hires, Máté
Drotár, Peter
Clasificación UNESCO: 320711 Neuropatología
570110 Patología y corrección del lenguaje
Fecha de publicación: 2022
Conferencia: 20th Conference of the International Graphonomics Society (IGS 2021) 
Resumen: This paper proposes the ensemble of deep convolutional neural networks for diagnosing Parkinson’s disease from offline handwriting. The advantage of the offline approach lies in the fact that handwriting acquisition can be performed without any specialized equipment by using only a smartphone camera. The convolutional neural networks ensemble relies on pre-trained networks where the diversity is achieved through the multiple-fine-tuning of individual networks. The experimental results on two handwriting datasets showed that the proposed approach currently provides the highest classification accuracy compared to other strategies for diagnosing Parkinson’s disease based on offline handwriting.
URI: http://hdl.handle.net/10553/116094
Fuente: The 20th Conference of the International Graphonomics Society (IGS2021). Conference proceedings for short papers not published in the LNCS – Springer
Colección:Actas de congresos
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