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27/100

✓ Peer-reviewed alzhub usefulness score 27 out of 100. Brain scans and imaging

Topic: Amyloid, tau and blood-test diagnosis

A progressive neurological disease, Alzheimer's disease (AD) is identified by cognitive decline and memory loss.Effective intervention and better patient outcomes depend on early diagnosis.In this work, the application of Vision Transformers (ViTs) to MRI scans for early AD identification is investigated.Compared to conventional architectures, ViTs offer advantages by utilizing self-attention processes to gather global picture features.The model was developed and evaluated on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset that cover four stages of disease which are Cognitively Normal (CN), Early Mild Cognitive Impairment (EMCI), Late Mild Cognitive Impairment (LMCI), and Alzheimer's Disease (AD).The results demonstrate that the ViT model outperforms conventional Convolutional Neural Networks (CNNs) like DenseNet-121 and VGG-16, achieving 95% accuracy and a 96% F1-score.It reveals strong performance in detecting early-stage AD and offers interpretability through attention maps highlighting key brain regions.This study underscores the potential of ViT-based models to enhance diagnostic accuracy and clinical decision-making in Alzheimer's disease. Key words: -Alzheimer's disease (AD)

Key findings

A progressive neurological disease, Alzheimer's disease (AD) is identified by cognitive decline and memory loss.Effective intervention and better patient outcomes depend on early diagnosis.In this work, the application of Vision Transformers (ViTs) to MRI scans for early AD identification is investigated.Compared to conventional architectures, ViTs offer advantages by utilizing self-attention processes to gather global picture features.The model was developed and evaluated on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset that cover four stages of disease which are Cognitively Normal (CN), Early Mild Cognitive Impairment (EMCI), Late Mild Cognitive Impairment (LMCI), and Alzheimer's Disease (AD).The results demonstrate that the ViT model outperforms conventional Convolutional Neural Networks (CNNs) like DenseNet-121 and VGG-16, achieving 95% accuracy and a 96% F1-score.It reveals strong performance in detecting early-stage AD and offers interpretability through attention maps highlighting key brain regions.This study underscores the potential of ViT-based models to enhance diagnostic accuracy and clinical decision-making in Alzheimer's disease.

Why it matters

This Vision Transformer model classified MRI scans across four disease stages with 95% accuracy and a 96% F1-score, outperforming conventional CNNs. It is a diagnostic tool and does not report any treatment, cure, or prevention of Alzheimer's disease.

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Study details

Evidence strengthLow

How this study scored 27/100

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 and institution's track record, so those were left out: it earned 25 of the 93 points we could judge it on, which is 27 out of 100.

Component	Points	Why
Journal tier	7 / 10	Tier 3 — peer-reviewed journal (International Journal of Research Publication and Reviews)
Journal's citation record	0 / 4	International Journal of Research Publication and Reviews: journal h-index 30 (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	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	0 / 7	Cited 0 times (OpenAlex, 2026-10-10)
Citations per year	0 / 5	0.0 citations a year over 3 year(s) since 2025
Impact for its field and year	0 / 6	Cited 0.0× 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 0 times in 2024–2026 (2026 still in progress; OpenAlex)
Senior author's track record	— / 4	OpenAlex's profile for last-listed author K. Khudhuwanth holds only 1 work(s), too few to judge a track record — left out of this score
Institution's track record	— / 3	No institution listed in OpenAlex — left out of this score
Multi-centre collaboration	0 / 3	OpenAlex lists 0 institution(s) in 0 countries — scores zero (affiliation lists can be incomplete)
Indexed in PubMed	1 / 2	Not in PubMed, but has a DOI
Free to read	2 / 2	Free to read
Points	25 / 93	Scored on 18 of 20 parts. We have no data for the rest, so they are left out: 25 of the 93 points we could judge it on is 27 out of 100.
Total	27 / 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

Reddy, G. N., Pallavi, B., Nageswari, B., & Khudhuwanth, K. (2025). A Robust Deep Vision Transformer Model For Early Diagnosis Of Alzheimers Disease. International Journal of Research Publication and Reviews. https://doi.org/10.55248/gengpi.6.0425.16133

Vancouver

Reddy GN, Pallavi B, Nageswari B, Khudhuwanth K. A Robust Deep Vision Transformer Model For Early Diagnosis Of Alzheimers Disease. International Journal of Research Publication and Reviews. 2025. doi:10.55248/gengpi.6.0425.16133.

BibTeX

@article{reddy2025-70cc7b1b,
  author  = {Reddy, G. Nagarjuna and Pallavi, B. and Nageswari, B. and Khudhuwanth, K.},
  title   = {{A Robust Deep Vision Transformer Model For Early Diagnosis Of Alzheimers Disease}},
  journal = {International Journal of Research Publication and Reviews},
  year    = {2025},
  doi     = {10.55248/gengpi.6.0425.16133},
  url     = {https://doi.org/10.55248/gengpi.6.0425.16133}
}

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