EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 392 (2024) MATEC Web Conf., 392 (2024) 01132 References
Open Access
Issue
MATEC Web Conf.
Volume 392, 2024
International Conference on Multidisciplinary Research and Sustainable Development (ICMED 2024)
Article Number 01132
Number of page(s) 12
DOI https://doi.org/10.1051/matecconf/202439201132
Published online 18 March 2024
  1. WHO: Dementia. World Health Organization. https://www.who.int/news-room/factsheets/detail/dementia (2023). [Google Scholar]
  2. Lo, Raymond Y. “The borderland between normal aging and dementia.” Tzu-Chi Medical Journal 29, no. 2 (2017): 65. [Google Scholar]
  3. Chiu, Ming-Jang, Ta-Fu Chen, Ping-Keung Yip, Mau-Sun Hua, and Li-Yu Tang. “Behavioral and psychologic symptoms in different types of dementia.” Journal of the Formosan Medical Association 105, no. 7 (2006): 556-562. [CrossRef] [Google Scholar]
  4. Breijyeh, Zeinab, and Rafik Karaman. “Comprehensive review on Alzheimer’s disease: Causes and treatment.” Molecules 25, no. 24 (2020): 5789. [CrossRef] [Google Scholar]
  5. Silva, Nárlon C. Boa Sorte, Oliver Bracko, Amy R. Nelson, Fabricio Ferreira de Oliveira, Lisa S. Robison, C. Elizabeth Shaaban, Atticus H. Hainsworth, and Brittani R. Price. “Vascular cognitive impairment and dementia: An early career researcher perspective.” Alzheimer’s & dementia: diagnosis, assessment & disease monitoring 14, no. 1 (2022): e12310. [CrossRef] [Google Scholar]
  6. Barker, Megan S., Shana G. Dodge, Debra Niehoff, Sharon Denny, Penny A. Dacks, Susan Dickinson, Stephanie Cosentino, and Dianna KH Wheaton. “Living With Frontotemporal Degeneration: Diagnostic Journey, Symptom Experiences, and Disease Impact.” Journal of Geriatric Psychiatry and Neurology 36, no. 3 (2023): 201-214. [CrossRef] [Google Scholar]
  7. Grossman, Murray, William W. Seeley, Adam L. Boxer, Argye E. Hillis, David S. Knopman, Peter A. Ljubenov, Bruce Miller et al. “Frontotemporal lobar degeneration.” Nature Reviews Disease Primers 9, no. 1 (2023): 40. [CrossRef] [Google Scholar]
  8. Surendranathan, Ajenthan, Joseph PM Kane, Allison Bentley, Sally AH Barker, John-Paul Taylor, Alan J. Thomas, Louise M. Allan et al. “Clinical diagnosis of Lewy body dementia.” BJPsych open 6, no. 4 (2020): e61. [CrossRef] [Google Scholar]
  9. Taylor, John-Paul, Ian G. McKeith, David J. Burn, Brad F. Boeve, Daniel Weintraub, Claire Bamford, Louise M. Allan, Alan J. Thomas, and John T O’Brien. “New evidence on the management of Lewy body dementia.” The Lancet Neurology 19, no. 2 (2020): 157-169. [CrossRef] [Google Scholar]
  10. Gao, Xingyu, Hongjie Cai, and Manhua Liu. “A Hybrid Multi-scale Attention Convolution and Aging Transformer Network for Alzheimer’s Disease Diagnosis.” IEEE Journal of Biomedical and Health Informatics (2023). [Google Scholar]
  11. Mccombe, Niamh, Xuemei Ding, Girijesh Prasad, Paddy Gillespie, David P. Finn, Stephen Todd, Paula L. Mcclean, and Kongfatt Wong-Lin. “Alzheimer’s disease assessments optimized for diagnostic accuracy and administration time.” IEEE Journal of Translational Engineering in Health and Medicine 10 (2022): 1-9. [CrossRef] [Google Scholar]
  12. Klepl, Dominik, Fei He, Min Wu, Daniel J. Blackburn, and Ptolemaios Sarrigiannis. “Eeg-based graph neural network classification of alzheimer’s disease: An empirical evaluation of functional connectivity methods.” IEEE Transactions on Neural Systems and Rehabilitation Engineering 30 (2022): 2651-2660. [CrossRef] [Google Scholar]
  13. Sharma, Neelam, Maheshkumar H. Kolekar, and Kamlesh Jha. “Iterative filtering decomposition based early dementia diagnosis using EEG with cognitive tests.” IEEE Transactions on Neural Systems and Rehabilitation Engineering 28, no. 9 (2020): 1890-1898. [CrossRef] [Google Scholar]
  14. Pan, Xiaoxi, Trong-Le Phan, Mouloud Adel, Caroline Fossati, Thierry Gaidon, Julien Wojak, and Eric Guedj. “Multi-view separable pyramid network for AD prediction at MCI stage by 18 F-FDG brain PET imaging.” IEEE Transactions on Medical Imaging 40, no. 1 (2020): 81-92. [Google Scholar]
  15. Kuo, Ping-Huan, Chen-Ting Huang, and Ting-Chun Yao. “Optimized Transfer Learning Based Dementia Prediction System for Rehabilitation Therapy Planning.” IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023). [Google Scholar]
  16. Xia, Zhengwang, Tao Zhou, Saqib Mamoon, and Jianfeng Lu. “Recognition of dementia biomarkers with deep finer-DBN.” IEEE Transactions on Neural Systems and Rehabilitation Engineering 29 (2021): 1926-1935. [CrossRef] [Google Scholar]
  17. Noella, RS Nancy, and J. Priyadarshini. “Diagnosis of Alzheimer’s, Parkinson’s disease and frontotemporal dementia using a generative adversarial deep convolutional neural network.” Neural Computing and Applications 35, no. 3 (2023): 2845-2854. [CrossRef] [Google Scholar]
  18. Yang, Hui, and Peter A. Bath. “The use of data mining methods for the prediction of dementia: evidence from the english longitudinal study of aging.” IEEE journal of biomedical and health informatics 24, no. 2 (2019): 345-353. [Google Scholar]
  19. Javeed, Ashir, Ana Luiza Dallora, Johan Sanmartin Berglund, Arif Ali, Liaqata Ali, and Peter Anderberg. “Machine Learning for Dementia Prediction: A Systematic Review and Future Research Directions.” Journal of medical systems 47, no. 1 (2023): 17. [CrossRef] [Google Scholar]
  20. Ni, Yu-Ching, Fan-Pin Tseng, Ming-Chyi Pai, Ing-Tsung Hsiao, Kun-Ju Lin, Zhi-Kun Lin, Chia-Yu Lin et al. “The feasibility of differentiating Lewy body dementia and Alzheimer’s disease by deep learning using ECD SPECT images.” Diagnostics 11, no. 11 (2021): 2091. [CrossRef] [Google Scholar]
  21. Castellazzi, Gloria, Maria Giovanna Cuzzoni, Matteo Cotta Ramusino, Daniele Martinelli, Federica Denaro, Antonio Ricciardi, Paolo Vitali et al. “A machine learning approach for the differential diagnosis of Alzheimer and vascular dementia fed by MRI selected features.” Frontiers in neuroinformatics (2020): 25. [Google Scholar]
  22. Huy, Vo Trong Quang, and Chih-Min Lin. “An Improved Densenet Deep Neural Network Model for Tuberculosis Detection Using Chest X-Ray Images.” IEEE Access (2023). [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.