The DOI matches its Crossref record; 50 of the 50 references it cites were found in a registry or an index.
A Transfer Learning Approach for Early Diagnosis of Alzheimer’s Disease on MRI Images
Open accessAbstract
Mild cognitive impairment (MCI) detection using magnetic resonance image (MRI), plays a crucial role in the treatment of dementia disease at an early stage. Deep learning architecture produces impressive results in such research. Algorithms require a large number of annotated datasets for training the model. In this study, we overcome this issue by using layer-wise transfer learning as well as tissue segmentation of brain images to diagnose the early stage of Alzheimer's disease (AD). In layer-wise transfer learning, we used the VGG architecture family with pre-trained weights. The proposed model segregates between normal control (NC), the early mild cognitive impairment (EMCI), the late mild cognitive impairment (LMCI), and the AD. In this paper, 85 NC patients, 70 EMCI, 70 LMCI, and 75 AD patients access form the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Tissue segmentation was applied on each subject to extract the gray matter (GM) tissue. In order to check the validity, the proposed method is tested on preprocessing data and achieved the highest rates of the classification accuracy on AD vs NC is 98.73%, also distinguish between EMCI vs LMCI patients testing accuracy 83.72% , whereas remaining classes accuracy is more than 80%. Finally, we provide a comparative analysis with other studies which shows that the proposed model outperformed the state-of-the-art models in terms of testing accuracy.
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Cites
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Cited by
Lekadir, K., Frangi, A. F., Porras, A. R., Glocker, B., Cintas, C., Langlotz, C. P., Weicken, E., Asselbergs, F. W., Prior, F., Collins, G. S., et al. (2025) · BMJ · doi:10.1136/bmj-2024-081554 · Cited by 957
Irmak, E. (2021) · Iranian Journal of Science and Technology, Transactions of Electrical Engineering · doi:10.1007/s40998-021-00426-9 · Cited by 477
Transfer learning techniques for medical image analysis: A review
Kora, P., Ooi, C. P., Faust, O., Raghavendra, U., Gudigar, A., Chan, W. Y., Meenakshi, K., Swaraja, K., Plawiak, P., & Rajendra Acharya, U. (2022) · Biocybernetics and Biomedical Engineering · doi:10.1016/j.bbe.2021.11.004 · Cited by 367
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
Kaur, A., Mittal, M., Bhatti, J. S., Thareja, S., & Singh, S. (2024) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2024.102928 · Cited by 54
Metadata
- Authors
- Mehmood, A., Yang, S., Feng, Z., Wang, M., Ahmad, A. S., Khan, R., Maqsood, M., & Yaqub, M.
- Venue
- Neuroscience
- Publisher
- Elsevier BV
- Published
- 2021-04
- Volume
- 460
- Pages
- 43-52
- ISSN
- 0306-4522 (ISSN-L)
- OpenAlex
- W3121966255
- Cited by
- 292
- References
- 50
- FWCI
- 31.73
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Mehmood, A., Yang, S., Feng, Z., Wang, M., Ahmad, A. S., Khan, R., Maqsood, M., & Yaqub, M. (2021). A Transfer Learning Approach for Early Diagnosis of Alzheimer’s Disease on MRI Images. Neuroscience. https://doi.org/10.1016/j.neuroscience.2021.01.002
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