Cargando…

Automated Medical Diagnosis of Alzheimer´s Disease Using an Efficient Net Convolutional Neural Network

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 nove...

Descripción completa

Detalles Bibliográficos
Autores principales: Agarwal, Deevyankar, Berbís, Manuel Álvaro, Luna, Antonio, Lipari, Vivian, Ballester, Julien Brito, de la Torre-Díez, Isabel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10154284/
https://www.ncbi.nlm.nih.gov/pubmed/37129723
http://dx.doi.org/10.1007/s10916-023-01941-4
_version_ 1785036095318130688
author Agarwal, Deevyankar
Berbís, Manuel Álvaro
Luna, Antonio
Lipari, Vivian
Ballester, Julien Brito
de la Torre-Díez, Isabel
author_facet Agarwal, Deevyankar
Berbís, Manuel Álvaro
Luna, Antonio
Lipari, Vivian
Ballester, Julien Brito
de la Torre-Díez, Isabel
author_sort Agarwal, Deevyankar
collection PubMed
description 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.
format Online
Article
Text
id pubmed-10154284
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Springer US
record_format MEDLINE/PubMed
spelling pubmed-101542842023-05-04 Automated Medical Diagnosis of Alzheimer´s Disease Using an Efficient Net Convolutional Neural Network Agarwal, Deevyankar Berbís, Manuel Álvaro Luna, Antonio Lipari, Vivian Ballester, Julien Brito de la Torre-Díez, Isabel J Med Syst Original Paper 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. Springer US 2023-05-02 2023 /pmc/articles/PMC10154284/ /pubmed/37129723 http://dx.doi.org/10.1007/s10916-023-01941-4 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Original Paper
Agarwal, Deevyankar
Berbís, Manuel Álvaro
Luna, Antonio
Lipari, Vivian
Ballester, Julien Brito
de la Torre-Díez, Isabel
Automated Medical Diagnosis of Alzheimer´s Disease Using an Efficient Net Convolutional Neural Network
title Automated Medical Diagnosis of Alzheimer´s Disease Using an Efficient Net Convolutional Neural Network
title_full Automated Medical Diagnosis of Alzheimer´s Disease Using an Efficient Net Convolutional Neural Network
title_fullStr Automated Medical Diagnosis of Alzheimer´s Disease Using an Efficient Net Convolutional Neural Network
title_full_unstemmed Automated Medical Diagnosis of Alzheimer´s Disease Using an Efficient Net Convolutional Neural Network
title_short Automated Medical Diagnosis of Alzheimer´s Disease Using an Efficient Net Convolutional Neural Network
title_sort automated medical diagnosis of alzheimer´s disease using an efficient net convolutional neural network
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10154284/
https://www.ncbi.nlm.nih.gov/pubmed/37129723
http://dx.doi.org/10.1007/s10916-023-01941-4
work_keys_str_mv AT agarwaldeevyankar automatedmedicaldiagnosisofalzheimersdiseaseusinganefficientnetconvolutionalneuralnetwork
AT berbismanuelalvaro automatedmedicaldiagnosisofalzheimersdiseaseusinganefficientnetconvolutionalneuralnetwork
AT lunaantonio automatedmedicaldiagnosisofalzheimersdiseaseusinganefficientnetconvolutionalneuralnetwork
AT liparivivian automatedmedicaldiagnosisofalzheimersdiseaseusinganefficientnetconvolutionalneuralnetwork
AT ballesterjulienbrito automatedmedicaldiagnosisofalzheimersdiseaseusinganefficientnetconvolutionalneuralnetwork
AT delatorrediezisabel automatedmedicaldiagnosisofalzheimersdiseaseusinganefficientnetconvolutionalneuralnetwork