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Progression-Aware and Explainable CNN–Transformer Framework for Multiclass Alzheimer’s Disease Staging Using MRI
Khalaf Okab Alsalem, Murtada Khalafallah Elbashir, Ahmed Omar Alzahrani, Mohanad Mohammed, Mahmood A. Mahmood, Tarek Abd El fattah. Diagnostics, 2026-02-16
Topic: Amyloid, tau and blood-test diagnosis
The experimental findings on a multiclass MRI dataset of Alzheimer disease demonstrate consistent and high performance across cross-validation folds (mean F1-score (0.991 ± 0.003) and AUROC (0.9998 ± 0.0002)), without losing transparency and progress awareness. The proposed framework provided a robust and interpretable method of Alzheimer disease severity classification using MRI.
Key findings
- The experimental findings on a multiclass MRI dataset of Alzheimer disease demonstrate consistent and high performance across cross-validation folds (mean F1-score (0.991 ± 0.003) and AUROC (0.9998 ± 0.0002)), without losing transparency and progress awareness.
- The proposed framework provided a robust and interpretable method of Alzheimer disease severity classification using MRI.
Why it matters
This study used a combined deep learning approach to classify Alzheimer's disease severity from MRI scans, reporting high performance across validation folds. It is a classification method only and does not test any treatment, cure, or prevention.
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Study details
- Evidence strengthLow
How this study scored 35/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it. We have no data on senior author's track record, so that was left out: it earned 34 of the 96 points we could judge it on, which is 35 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Diagnostics) |
| Journal's citation record | 1 / 4 | Diagnostics: journal h-index 129 (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 | 5 / 5 | 2026 — within 1 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 | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 1 / 7 | Cited 1 times (OpenAlex, 2026-10-10) |
| Citations per year | 0 / 5 | 0.5 citations a year over 2 year(s) since 2026 |
| Impact for its field and year | 3 / 6 | Cited 4.7× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 0 / 4 | Cited 1 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | — / 4 | OpenAlex's profile for last-listed author Tarek Abd El fattah holds only 2 work(s), too few to judge a track record — left out of this score |
| Institution's track record | 0 / 3 | University of KwaZulu-Natal: institution h-index 580 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 4 institution(s) in 3 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 41750741) |
| Free to read | 2 / 2 | Free to read |
| Points | 34 / 96 | Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 34 of the 96 points we could judge it on is 35 out of 100. |
| Total | 35 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.
APA 7th
Alsalem, K. O., Elbashir, M. K., Alzahrani, A. O., Mohammed, M., Mahmood, M. A., & El fattah, T. A. (2026). Progression-Aware and Explainable CNN–Transformer Framework for Multiclass Alzheimer’s Disease Staging Using MRI. Diagnostics. https://doi.org/10.3390/diagnostics16040593
Vancouver
Alsalem KO, Elbashir MK, Alzahrani AO, Mohammed M, Mahmood MA, El fattah TA. Progression-Aware and Explainable CNN–Transformer Framework for Multiclass Alzheimer’s Disease Staging Using MRI. Diagnostics. 2026. doi:10.3390/diagnostics16040593. PMID: 41750741.
BibTeX
@article{alsalem2026-41750741,
author = {Alsalem, Khalaf Okab and Elbashir, Murtada Khalafallah and Alzahrani, Ahmed Omar and Mohammed, Mohanad and Mahmood, Mahmood A. and El fattah, Tarek Abd},
title = {{Progression-Aware and Explainable CNN–Transformer Framework for Multiclass Alzheimer’s Disease Staging Using MRI}},
journal = {Diagnostics},
year = {2026},
doi = {10.3390/diagnostics16040593},
pmid = {41750741},
url = {https://doi.org/10.3390/diagnostics16040593}
}
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