Binary Classification of Alzheimer's Disease Using sMRI Imaging Modality and Deep Learning.
Alzheimer's disease (AD) is an irreversible devastative neurodegenerative disorder associated with progressive impairment of memory and cognitive functions. Its early diagnosis is crucial for the development of possible future treatment option(s). Structural magnetic resonance images (sMRI) play an...
| Publicado en: | Journal of Digital Imaging Vol. 33; no. 5; pp. 1073 - 1091 |
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| Autores principales: | , , |
| Formato: | diagnostic images equations & formulas review tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2020
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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=146532209&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 146532209 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Oct2020 vid: 33 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 146532209 144818373 146532209 146532209 10.1007/s10278-019-00265-5 146532209 ppf: 1073 ppct: 18 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Binary Classification of Alzheimer's Disease Using sMRI Imaging Modality and Deep Learning. aug: au: Tufail, Ahsan Bin Ma, Yong-Kui Zhang, Qiu-Na affil: Harbin Institute of Technology, Harbin, China sug: subj: Deep Learning Alzheimer's Disease Classification Magnetic Resonance Imaging Methods Neural Networks (Computer) Memory Cognition Alzheimer's Disease Physiopathology Gray Matter ab: Alzheimer's disease (AD) is an irreversible devastative neurodegenerative disorder associated with progressive impairment of memory and cognitive functions. Its early diagnosis is crucial for the development of possible future treatment option(s). Structural magnetic resonance images (sMRI) play an important role to help in understanding the anatomical changes related to AD especially in its early stages. Conventional methods require the expertise of domain experts and extract hand-picked features such as gray matter substructures and train a classifier to distinguish AD subjects from healthy subjects. Different from these methods, this paper proposes to construct multiple deep 2D convolutional neural networks (2D-CNNs) to learn the various features from local brain images which are combined to make the final classification for AD diagnosis. The whole brain image was passed through two transfer learning architectures; Inception version 3 and Xception, as well as a custom Convolutional Neural Network (CNN) built with the help of separable convolutional layers which can automatically learn the generic features from imaging data for classification. Our study is conducted using cross-sectional T1-weighted structural MRI brain images from Open Access Series of Imaging Studies (OASIS) database to maintain the size and contrast over different MRI scans. Experimental results show that the transfer learning approaches exceed the performance of non-transfer learning-based approaches demonstrating the effectiveness of these approaches for the binary AD classification task. pubtype: Academic Journal doctype: diagnostic images equations & formulas review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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