Convolutional Neural Networks; deep feature concatenation; Deep Learning; dementia; image processing; Alzheimers disease; Convolutional neural network; Data augmentation; Deep feature concatenation; Deep learning; Dementia; Disease detection; Handwriting input; Images processing; Input sources; Artificial Intelligence; Computer Science Applications; Computer Vision and Pattern Recognition; Information Systems; Electrical and Electronic Engineering; Modeling and Simulation
Abstract :
[en] Alzheimer's disease (AD) is a cognitive disorder, marked by memory loss and impaired reasoning, that requires early detection methods to better manage and potentially slow down the disease's progression. Recent advances in machine learning have offered new possibilities for AD detection using handwriting analysis, however previous work has considered only one type of input source, e.g. clock or pentagon drawings. Here we propose to develop an efficient method for detecting AD's early symptoms using Deep Feature Concatenation (DFC) models considering multiple handwriting sources: pentagon drawings, self-reported sentences, and signatures. Substantial performance improvements were observed when considering all input sources together with data augmentation techniques. For example, classification accuracy increased from 60% (best model, without data augmentation) to 80% (DFC and data augmentation). Our findings show that the use of diverse input sources can lead to an efficient and cost-effective method for early AD detection. Looking forward into the future, our study highlights the potential of DFC in supporting home-based healthcare diagnoses which is a crucial step in integrating artificial intelligence into healthcare practices.
Disciplines :
Computer science
Author, co-author :
HOSSEINI KIVANANI, Nina ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS)
Salobrar-García, Elena; Ramon Castroviejo Institute of Ophthalmologic Research, Universidad Complutense of Madrid, Spain
Elvira-Hurtado, Lorena; Ramon Castroviejo Institute of Ophthalmologic Research, Universidad Complutense of Madrid, Spain
López-Cuenca, Inés; Ramon Castroviejo Institute of Ophthalmologic Research, Universidad Complutense of Madrid, Spain
De Hoz, Rosa; Ramon Castroviejo Institute of Ophthalmologic Research, Universidad Complutense of Madrid, Spain
Ramírez, José M.; Ramon Castroviejo Institute of Ophthalmologic Research, Universidad Complutense of Madrid, Spain
Gil, Pedro; Memory Unit, Geriatrics Service, Hospital Clínico San Carlos, Madrid, Spain
Salas, Mario; Memory Unit, Geriatrics Service, Hospital Clínico San Carlos, Madrid, Spain
SCHOMMER, Christoph ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS)
LEIVA, Luis A. ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS)
External co-authors :
yes
Language :
English
Title :
Better Together: Combining Different Handwriting Input Sources Improves Dementia Screening
Publication date :
09 October 2023
Event name :
2023 IEEE 19th International Conference on e-Science (e-Science)
Event place :
Limassol, Cyp
Event date :
09-10-2023 => 14-10-2023
By request :
Yes
Audience :
International
Main work title :
Proceedings 2023 IEEE 19th International Conference on e-Science, e-Science 2023
Publisher :
Institute of Electrical and Electronics Engineers Inc.
IEEE Computer Society IEEE's Technical Committee on High-Performance Computing (TCHPC)
Funding text :
Work supported by the UCM research group (Grupo de Investigación básica en Ciencias de la Visión del IIORC, UCM-GR17-920105), the Horizon 2020 FET program of the European Union (grant CHIST-ERA-20-BCI-001), and the European Innovation Council Pathfinder program (grant 101071147).
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