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 novel app...
| Publicado en: | Journal of Medical Systems Vol. 47; no. 1; pp. 1 - 23 |
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| Autores principales: | , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Dec2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=163474424&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 163474424 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Dec2023 vid: 47 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 163474424 10.1007/s10916-023-01941-4 163474424 ppf: 1 ppct: 22 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Automated Medical Diagnosis of Alzheimer´s Disease Using an Efficient Net Convolutional Neural Network. aug: au: Agarwal, Deevyankar Berbís, Manuel Álvaro Luna, Antonio Lipari, Vivian Ballester, Julien Brito de la Torre-Díez, Isabel affil: Department of Signal Theory and Communications and Telematics Engineering, University of Valladolid, Paseo de Belén 15, 47011, Valladolid, Spain sug: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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