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A novel neuroimaging based early detection framework for alzheimer disease using deep learning
Areej Alasiry, Khlood Shinan, Abeer Abdullah Alsadhan, Hanan E. Alhazmi, Fatmah Alanazi, Muhammad Usman Ashraf, Taseer Muhammad. Scientific Reports, 2025-07-02
Topic: Amyloid, tau and blood-test diagnosis
Alzheimer's disease (AD) is a progressive neurodegenerative disorder that significantly impacts cognitive function, posing a major global health challenge. Despite its rising prevalence, particularly in low and middle-income countries, early diagnosis remains inadequate, with projections estimating over 55 million affected individuals by 2022, expected to triple by 2050. Accurate early detection is critical for effective intervention. This study presents Neuroimaging-based Early Detection of Alzheimer's Disease using Deep Learning (NEDA-DL), a novel computer-aided diagnostic (CAD) framework leveraging a hybrid ResNet-50 and AlexNet architecture optimized with CUDA-based parallel processing. The proposed deep learning model processes MRI and PET neuroimaging data, utilizing depthwise separable convolutions to enhance computational efficiency. Performance evaluation using key metrics including accuracy, sensitivity, specificity, and F1-score demonstrates state-of-the-art classification performance, with the Softmax classifier achieving 99.87% accuracy. Comparative analyses further validate the superiority of NEDA-DL over existing methods. By integrating structural and functional neuroimaging insights, this approach enhances diagnostic precision and supports clinical decision-making in Alzheimer's disease detection.
Key findings
- Alzheimer's disease (AD) is a progressive neurodegenerative disorder that significantly impacts cognitive function, posing a major global health challenge.
Why it matters
This study presents a deep learning framework using MRI and PET data for early detection of Alzheimer's disease and reports 99.87% accuracy with a Softmax classifier. It is a technical diagnostic study and does not test any treatment, cure, or prevention.
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Study details
- Effect size99.87%
- Evidence strengthLow
How this study scored 47/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, studied in people and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Scientific Reports) |
| Journal's citation record | 3 / 4 | Scientific Reports: journal h-index 463 (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 | 4 / 5 | 2025 — within 3 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) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 3 / 7 | Cited 15 times (OpenAlex, 2026-10-10) |
| Citations per year | 2 / 5 | 5.0 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 4 / 6 | Cited 9.5× 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 | 2 / 4 | Cited 15 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 3 / 4 | Last-listed author Taseer Muhammad: h-index 69 (OpenAlex, whole career) |
| Institution's track record | 0 / 3 | Imam Abdulrahman Bin Faisal University: institution h-index 222 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 5 institution(s) in 2 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 40596195) |
| Free to read | 2 / 2 | Free to read |
| Total | 47 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1038/s41598-025-05529-5
- PubMed: 40596195
- Original source →
- Free full text: Scientific Reports →
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. The record names 7 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.
APA 7th
Alasiry, A., Shinan, K., Alsadhan, A. A., Alhazmi, H. E., Alanazi, F., Ashraf, M. U., et al. (2025). A novel neuroimaging based early detection framework for alzheimer disease using deep learning. Scientific Reports. https://doi.org/10.1038/s41598-025-05529-5
Vancouver
Alasiry A, Shinan K, Alsadhan AA, Alhazmi HE, Alanazi F, Ashraf MU, et al. A novel neuroimaging based early detection framework for alzheimer disease using deep learning. Scientific Reports. 2025. doi:10.1038/s41598-025-05529-5. PMID: 40596195.
BibTeX
@article{alasiry2025-40596195,
author = {Alasiry, Areej and Shinan, Khlood and Alsadhan, Abeer Abdullah and Alhazmi, Hanan E. and Alanazi, Fatmah and Ashraf, Muhammad Usman and Muhammad, Taseer},
title = {{A novel neuroimaging based early detection framework for alzheimer disease using deep learning}},
journal = {Scientific Reports},
year = {2025},
doi = {10.1038/s41598-025-05529-5},
pmid = {40596195},
url = {https://doi.org/10.1038/s41598-025-05529-5}
}
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