2026-04-14T15:00:03Zhttps://uvadoc.uva.es/oai/request

oai:uvadoc.uva.es:10324/596642023-06-12T08:58:40Zcom_10324_1191com_10324_931com_10324_894col_10324_1379

Agarwal, Deevyankar Berbís, Manuel Álvaro Luna, Antonio Lipari, Vivian Brito Ballester, Julien Torre Díez, Isabel de la 2023-05-22T09:19:45Z 2023-05-22T09:19:45Z 2023 Journal of Medical Systems, 2023, vol.47, n. 1, art. 57. 0148-5598 https://uvadoc.uva.es/handle/10324/59664 10.1007/s10916-023-01941-4 1 Journal of Medical Systems 47 1573-689X Producción Científica 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. Publicación en abierto financiada por el Consorcio de Bibliotecas Universitarias de Castilla y León (BUCLE), con cargo al Programa Operativo 2014ES16RFOP009 FEDER 2014-2020 DE CASTILLA Y LEÓN, Actuación:20007-CL - Apoyo Consorcio BUCLE application/pdf eng Springer info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/4.0/ © 2023 The Author(s) Atribución 4.0 Internacional Alzheimer´s disease Convolutional neural network Deep learning EfficientNet Mild cognitive impairment MRI MONAI Transfer learning 33 Ciencias Tecnológicas Automated medical diagnosis of alzheimer´s disease using an Efficient Net convolutional neural network info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion https://link.springer.com/article/10.1007/s10916-023-01941-4 SI