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EARLY STAGES OF ALZHEIMERS DETECTION
Srimathi S, Shalini S, Shreeragavi K, Thahasin Begam A. Zenodo (CERN European Organization for Nuclear Research), 2025-11-20
Topic: Amyloid, tau and blood-test diagnosis
The Early Stages of Alzheimer’s Detection project focuses on identifying signs of Alzheimer’s disease at the earliest possible stage using advanced machine learning and medical imaging techniques. The system analyzes MRI brain images and predicts whether a patient shows early indicators of Alzheimer’s, enabling doctors to take timely action. This project uses a deep learning model based on EfficientNetV2-S, fine-tuned on a medically validated MRI dataset. The model is trained to recognize subtle patterns in the brain—such as structural shrinkage, texture changes, and abnormalities—associated with Alzheimer’s progression. To support real-time usage, the system includes a web-based interface where users can upload MRI scans and instantly receive classification results. The interface provides a clean, user-friendly design, making it suitable for medical professionals, researchers, and screening centers. The end goal of the project is to improve early detection, increase diagnosis accuracy, and support healthcare providers in making faster, data-driven decisions that could significantly slow the progression of Alzheimer’s disease.
Key findings
- The model is trained to recognize subtle patterns in the brain—such as structural shrinkage, texture changes, and abnormalities—associated with Alzheimer’s progression.
- To support real-time usage, the system includes a web-based interface where users can upload MRI scans and instantly receive classification results.
- The end goal of the project is to improve early detection, increase diagnosis accuracy, and support healthcare providers in making faster, data-driven decisions that could significantly slow the progression of Alzheimer’s disease.
Why it matters
This project describes a deep learning system that reads MRI brain images and predicts early signs of Alzheimer's, with a web interface for uploading scans. No results from testing the system on patients are reported, so it is not yet evidence that it improves diagnosis or slows the disease.
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Study details
- Evidence strengthLow
How this study scored 39/100
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 journal's citation record, times cited, citations per year, impact for its field and year, still being cited, senior author's track record, institution's track record and multi-centre collaboration, so those were left out: it earned 25 of the 64 points we could judge it on, which is 39 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Zenodo (CERN European Organization for Nuclear Research)) |
| Journal's citation record | — / 4 | No journal record from OpenAlex — left out of this score |
| 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 | 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 | — / 7 | No citation count (not found in OpenAlex) — left out of this score |
| Citations per year | — / 5 | No citation count or no year — left out of this score |
| Impact for its field and year | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | — / 4 | No yearly citation counts — left out of this score |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | — / 3 | No institution listed in OpenAlex — left out of this score |
| Multi-centre collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 1 / 2 | Not in PubMed, but has a DOI |
| Free to read | 2 / 2 | Free to read |
| Points | 25 / 64 | Scored on 12 of 20 parts. We have no data for the rest, so they are left out: 25 of the 64 points we could judge it on is 39 out of 100. |
| Total | 39 / 100 | — |
What each of these 20 components measures, and what this score is not →
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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
Srimathi, S., Shalini, S., Shreeragavi, K., & Thahasin Begam, A. (2025). EARLY STAGES OF ALZHEIMERS DETECTION. Zenodo (CERN European Organization for Nuclear Research). https://doi.org/10.5281/zenodo.17658790
Vancouver
Srimathi S, Shalini S, Shreeragavi K, Thahasin Begam A. EARLY STAGES OF ALZHEIMERS DETECTION. Zenodo (CERN European Organization for Nuclear Research). 2025. doi:10.5281/zenodo.17658790.
BibTeX
@article{srimathi2025-8654d553,
author = {Srimathi, S. and Shalini, S. and Shreeragavi, K. and Thahasin Begam, A.},
title = {{EARLY STAGES OF ALZHEIMERS DETECTION}},
journal = {Zenodo (CERN European Organization for Nuclear Research)},
year = {2025},
doi = {10.5281/zenodo.17658790},
url = {https://doi.org/10.5281/zenodo.17658790}
}
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