Research of spatial context convolutional neural networks for early diagnosis of Alzheimer’s disease-Reference-Cited by-同舟云学术

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Research of spatial context convolutional neural networks for early diagnosis of Alzheimer’s disease

Published:2023-09-26 Issue: Volume: Page:
ISSN:0920-8542
Container-title:The Journal of Supercomputing
language:en
Short-container-title:J Supercomput

Author:

Tong Yinsheng,Li Zuoyong,Huang Hui,Gao Libin,Xu Minghai,Hu Zhongyi

Funder

the Key Project of Zhejiang Provincial Natural Science Foundation under Grant

the National Natural Science Foundation of China

the Major Project of Wenzhou Natural Science Foundation

Natural Science Foundation of Fujian Province

Publisher

Springer Science and Business Media LLC

Subject

Hardware and Architecture,Information Systems,Theoretical Computer Science,Software

Link

https://link.springer.com/content/pdf/10.1007/s11227-023-05655-9.pdf

Reference41 articles.

1. Jo T, Nho K, Saykin AJ (2019) Deep learning in Alzheimer’s disease: diagnostic classification and prognostic prediction using neuroimaging data. Front Aging Neurosci 11:220. https://doi.org/10.3389/fnagi.2019.00220

2. Wen J, Thibeau-Sutre E, Diaz-Melo M, Samper-González J, Routier A, Bottani S, Dormont D, Durrleman S, Burgos N, Colliot O et al (2020) Convolutional neural networks for classification of Alzheimer’s disease: overview and reproducible evaluation. Med Image Anal 63:101694. https://doi.org/10.1016/j.media.2020.101694

3. Altinkaya E, Polat K, Barakli B (2020) Detection of Alzheimer’s disease and dementia states based on deep learning from MRI images: a comprehensive review. J Inst Electron Comput 1(1):39–53. https://doi.org/10.33969/JIEC.2019.11005

4. Murugan S, Venkatesan C, Sumithra M, Gao X-Z, Elakkiya B, Akila M, Manoharan S (2021) DEMNET: a deep learning model for early diagnosis of Alzheimer diseases and dementia from MR images. IEEE Access 9:90319–90329. https://doi.org/10.1109/ACCESS.2021.3090474.

5. Bhatt D, Patel C, Talsania H, Patel J, Vaghela R, Pandya S, Modi K, Ghayvat H (2021) CNN variants for computer vision: History, architecture, application, challenges and future scope. Electronics 10(20):2470. https://doi.org/10.3390/electronics10202470

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