RT info:eu-repo/semantics/article
T1 Transfer learning for Alzheimer’s disease through neuroimaging biomarkers: A systematic review
A1 Agarwal, Deevyankar
A1 Marques, Gonçalo
A1 Torre Díez, Isabel de la
A1 Franco Martín, Manuel Ángel
A1 Garcia Zapirain, Begonya
A1 Martín Rodríguez, Francisco
K1 Tecnología médica
K1 Neurosciences
K1 Image processing
K1 Alzheimer’s disease
K1 Neuroimaging biomarkers
K1 Transfer learning
K1 Enfermedad de Alzheimer
K1 Biomarcadores de neuroimagen
K1 Transferir el aprendizaje
K1 33 Ciencias Tecnológicas
AB Alzheimer’s disease (AD) is a remarkable challenge for healthcare in the 21st century. Since 2017, deep learning models with transfer learning approaches have been gaining recognition in AD detection, and progression prediction by using neuroimaging biomarkers. This paper presents a systematic review of the current state of early AD detection by using deep learning models with transfer learning and neuroimaging biomarkers. Five databases were used and the results before screening report 215 studies published between 2010 and 2020. After screening, 13 studies met the inclusion criteria. We noted that the maximum accuracy achieved to date for AD classification is 98.20% by using the combination of 3D convolutional networks and local transfer learning, and that for the prognostic prediction of AD is 87.78% by using pre-trained 3D convolutional network-based architectures. The results show that transfer learning helps researchers in developing a more accurate system for the early diagnosis of AD. However, there is a need to consider some points in future research, such as improving the accuracy of the prognostic prediction of AD, exploring additional biomarkers such as tau-PET and amyloid-PET to understand highly discriminative feature representation to separate similar brain patterns, managing the size of the datasets due to the limited availability.
PB MDPI
YR 2021
FD 2021
LK https://uvadoc.uva.es/handle/10324/59539
UL https://uvadoc.uva.es/handle/10324/59539
LA eng
NO Sensors 2021, vol. 21, n. 21, 7259
NO Producción Científica
DS UVaDOC
RD 15-ene-2025