RT info:eu-repo/semantics/article
T1 Automated medical diagnosis of alzheimer´s disease using an Efficient Net convolutional neural network
A1 Agarwal, Deevyankar
A1 Berbís, Manuel Álvaro
A1 Luna, Antonio
A1 Lipari, Vivian
A1 Brito Ballester, Julien
A1 Torre Díez, Isabel de la
K1 Alzheimer´s disease
K1 Convolutional neural network
K1 Deep learning
K1 EfficientNet
K1 Mild cognitive impairment
K1 MRI
K1 MONAI
K1 Transfer learning
K1 33 Ciencias Tecnológicas
AB Alzheimer's disease (AD) poses an enormous challenge to modern healthcare. Since 2017, researchers have been using deep learning (DL) models for the early detection of AD using neuroimaging biomarkers. In this paper, we implement the EfficietNet-b0 convolutional neural network (CNN) with a novel approach—"fusion of end-to-end and transfer learning"—to classify different stages of AD. 245 T1W MRI scans of cognitively normal (CN) subjects, 229 scans of AD subjects, and 229 scans of subjects with stable mild cognitive impairment (sMCI) were employed. Each scan was preprocessed using a standard pipeline. The proposed models were trained and evaluated using preprocessed scans. For the sMCI vs. AD classification task we obtained 95.29% accuracy and 95.35% area under the curve (AUC) for model training and 93.10% accuracy and 93.00% AUC for model testing. For the multiclass AD vs. CN vs. sMCI classification task we obtained 85.66% accuracy and 86% AUC for model training and 87.38% accuracy and 88.00% AUC for model testing. Based on our experimental results, we conclude that CNN-based DL models can be used to analyze complicated MRI scan features in clinical settings.
PB Springer
SN 0148-5598
YR 2023
FD 2023
LK https://uvadoc.uva.es/handle/10324/59664
UL https://uvadoc.uva.es/handle/10324/59664
LA eng
NO Journal of Medical Systems, 2023, vol.47, n. 1, art. 57.
NO Producción Científica
DS UVaDOC
RD 16-ago-2024