The DOI matches its Crossref record; 54 of the 54 references it cites were found in a registry or an index.
A Deep Learning Approach for Automated Diagnosis and Multi-Class Classification of Alzheimer’s Disease Stages Using Resting-State fMRI and Residual Neural Networks
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Deep Residual Learning for Image Recognition
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He, K., Zhang, X., Ren, S., & Sun, J. (2016) · arXiv · doi:10.48550/arxiv.1603.05027 · Cited by 10,665
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Research works printed but not linked: 1
- Oghabian, M. A., Batouli, S. A. H., Norouzian, M., Ziaei, M., and Sikaroodi, H., Using functional magnetic resonance imaging to differentiate between healthy aging subjects, Mild Cognitive Impairment, and Alzheimer’s patients. J. Res. Med. Sci. Off. J. Isfahan Univ. Med. Sci. 15(2):84, 2010.
Cited by
Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., & Ryll, M. (2023) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2023.3246839 · Cited by 231
Deep learning for Alzheimer's disease diagnosis: A survey
Khojaste-Sarakhsi, M., Haghighi, S. S., Ghomi, S. M. T. F., & Marchiori, E. (2022) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2022.102332 · Cited by 194
Celard, P., Iglesias, E. L., Sorribes-Fdez, J. M., Romero, R., Vieira, A. S., & Borrajo, L. (2023) · Neural Computing and Applications · doi:10.1007/s00521-022-07953-4 · Cited by 188
Functional magnetic resonance imaging, deep learning, and Alzheimer's disease: A systematic review
Warren, S. L., & Moustafa, A. A. (2023) · Journal of Neuroimaging · doi:10.1111/jon.13063 · Cited by 161
Deep fake detection and classification using error-level analysis and deep learning
Rafique, R., Gantassi, R., Amin, R., Frnda, J., Mustapha, A., & Alshehri, A. H. (2023) · Scientific Reports · doi:10.1038/s41598-023-34629-3 · Cited by 155
Metadata
- Authors
- Ramzan, F., Khan, M. U. G., Rehmat, A., Iqbal, S., Saba, T., Rehman, A., & Mehmood, Z.
- Venue
- Journal of Medical Systems
- Published
- 2019-12-18
- Volume
- 44
- Issue
- 2
- Pages
- 37-37
- ISSN
- 0148-5598 (ISSN-L)
- 1573-689X
- OpenAlex
- W2995864059
- Cited by
- 432
- References
- 54
- FWCI
- 23.02
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- www.springer.com/tdm (vor)
- Funders
- Higher Education Commission, Pakistan (NRPU-4223)
- Prince Sultan University
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- CoChat. (2026). Citation check: A Deep Learning Approach for Automated Diagnosis and Multi-Class Classification of Alzheimer’s Disease Stages Using Resting-State fMRI and Residual Neural Networks. https://app.cochat.ai/papers/10.1007/s10916-019-1475-2
- The paper
- Ramzan, F., Khan, M. U. G., Rehmat, A., Iqbal, S., Saba, T., Rehman, A., & Mehmood, Z. (2020). A Deep Learning Approach for Automated Diagnosis and Multi-Class Classification of Alzheimer’s Disease Stages Using Resting-State fMRI and Residual Neural Networks. Journal of Medical Systems. https://doi.org/10.1007/s10916-019-1475-2
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
