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A Transfer Learning Approach for Early Diagnosis of Alzheimer's Disease on MRI Images
Mehmood A, Yang S, Feng Z, Wang M, Ahmad AS, Khan R, Maqsood M, Yaqub M.. Neuroscience, 2021-01-17
Topic: Amyloid, tau and blood-test diagnosis
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.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Mehmood, A., Yang, S., Feng, Z., Wang, M., Ahmad, A. S., Khan, R., et al. (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
Vancouver
Mehmood A, Yang S, Feng Z, Wang M, Ahmad AS, Khan R, et al. A Transfer Learning Approach for Early Diagnosis of Alzheimer's Disease on MRI Images. Neuroscience. 2021. doi:10.1016/j.neuroscience.2021.01.002. PMID: 33465405.
BibTeX
@article{mehmood2021-33465405,
author = {Mehmood, A. and Yang, S. and Feng, Z. and Wang, M. and Ahmad, A. S. and Khan, R. and Maqsood, M. and Yaqub, M.},
title = {{A Transfer Learning Approach for Early Diagnosis of Alzheimer's Disease on MRI Images}},
journal = {Neuroscience},
year = {2021},
doi = {10.1016/j.neuroscience.2021.01.002},
pmid = {33465405},
url = {https://doi.org/10.1016/j.neuroscience.2021.01.002}
}
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. The record names 8 authors; APA and Vancouver show the first six, BibTeX and the downloads carry all of them. Check the citation against the original before you use it.
Key findings
- 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.
Why it matters
This work uses a deep learning method with transfer learning and brain tissue segmentation to help diagnose early-stage Alzheimer's disease on MRI images. It is a diagnostic tool only and does not test any treatment, cure, or prevention.
Study details
- Effect size98.73%; 83.72%
- Evidence strengthLow
How this study scored 51/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, studied in people and randomized / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Neuroscience) |
| Journal's citation record | 3 / 4 | Neuroscience: journal h-index 370 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 3 / 5 | 2021 — within 6 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 2 / 3 | Effect reported as a number, without an interval or test |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomized / blinded | 0 / 2 | No randomization or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 5 / 7 | Cited 293 times (OpenAlex, 2026-10-10) |
| Citations per year | 4 / 5 | 41.9 citations a year over 7 year(s) since 2021 |
| Impact for its field and year | 5 / 6 | Cited 31.9× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 2 / 6 | Cited by 3 other studies in this library |
| Still being cited | 3 / 4 | Cited 158 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 1 / 4 | Last-listed author Muhammad Yaqub: h-index 19 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | Huazhong University of Science and Technology: institution h-index 934 (OpenAlex) |
| Multicenter collaboration | 2 / 3 | OpenAlex lists 4 institution(s) in 2 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 33465405) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Total | 51 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1016/j.neuroscience.2021.01.002
- PubMed: 33465405
- Original source →
- Free full text: Neuroscience →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
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