RT info:eu-repo/semantics/article
T1 End-to-end deep learning architectures using 3D neuroimaging biomarkers for early Alzheimer’s diagnosis
A1 Agarwal, Deevyankar
A1 Berbís, Manuel Álvaro
A1 Martín Noguerol, Teodoro
A1 Luna, Antonio
A1 García Parrado, Sara Carmen
A1 Torre Díez, Isabel de la
K1 Brain - Diseases
K1 Cerebro - Enfermedades
K1 Alzheimer's disease
K1 Alzheimer's disease - Diagnosis
K1 Alzheimer, Enfermedad de
K1 Neural networks (Computer science)
K1 Convolutional neural network
K1 Redes neuronales (Informática)
K1 Machine learning
K1 Aprendizaje automático
K1 Artificial intelligence
K1 Mild cognitive impairment
K1 Magnetic resonance imaging
K1 Resonancia magnética
K1 Neurology
K1 Neuroimaging
K1 Image processing
K1 Imágenes, Tratamiento de las
K1 1203.17 Informática
K1 1203.04 Inteligencia Artificial
K1 3205.07 Neurología
K1 3314 Tecnología Médica
AB This study uses magnetic resonance imaging (MRI) data to propose end-to-end learning implementing volumetric convolutional neural network (CNN) models for two binary classification tasks: Alzheimer’s disease (AD) vs. cognitively normal (CN) and stable mild cognitive impairment (sMCI) vs. AD. The baseline MP-RAGE T1 MR images of 245 AD patients and 229 with sMCI were obtained from the ADNI dataset, whereas 245 T1 MR images of CN people were obtained from the IXI dataset. All of the images were preprocessed in four steps: N4 bias field correction, denoising, brain extraction, and registration. End-to-end-learning-based deep CNNs were used to discern between different phases of AD. Eight CNN-based architectures were implemented and assessed. The DenseNet264 excelled in both types of classification, with 82.5% accuracy and 87.63% AUC for training and 81.03% accuracy for testing relating to the sMCI vs. AD and 100% accuracy and 100% AUC for training and 99.56% accuracy for testing relating to the AD vs. CN. Deep learning approaches based on CNN and end-to-end learning offer a strong tool for examining minute but complex properties in MR images which could aid in the early detection and prediction of Alzheimer’s disease in clinical settings.
PB MDPI
SN 2227-7390
YR 2022
FD 2022
LK https://uvadoc.uva.es/handle/10324/61987
UL https://uvadoc.uva.es/handle/10324/61987
LA eng
NO Mathematics, 2022, Vol. 10, Nº. 15, 2575
NO Producción Científica
DS UVaDOC
RD 28-nov-2024