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30/100

✓ Peer-reviewed alzhub usefulness score 30 out of 100. Brain scans and imaging

Topic: Amyloid, tau and blood-test diagnosis

Alzheimer disease (AD) destroys brain cells and the patient’s memory is lost as a result of a progressive and incurable neurodegenerative disease. Magnetic Resonance Imaging (MRI) scan brain images can be analyzed with Artificial Intelligence (AI) technology to diagnose this disease and predict its progression. Early diagnosis and personalized treatment of AD based on MRI images are crucial for improving patient outcomes. To overcome these challenges, a novel Tetra Alzheimer disease Classification via Dilated coordinate Attention based RegNet (TACADR) technique has been proposed. The proposed method utilizes Leaky ShuffleNet for extracting structural and spatial features to improve the classification accuracy. Dilated coordinate attention based RegNet is utilized to classify Tetra classes of AD using MRI images. The proposed approach is superior in terms on accuracy, recall, specificity, precision, and F measure, according to experimental data on AD. Experimental results on ADNI-MRI dataset confirms that the TACDAR approach is superior compared to other datasets. The proposed technique improves the accuracy range of 13.5%, 11.8%, 10.8%, 8.64%, 0.45%, 3.88%, 2.78%, and 2.77% better than Custom CNN, TL-CCNN, EM-DL, PSO-CNN, BiLSTM-ANN, STCNN, ML-3DCNN, and LW-CNN respectively.

Key findings

Alzheimer disease (AD) destroys brain cells and the patient’s memory is lost as a result of a progressive and incurable neurodegenerative disease.
Experimental results on ADNI-MRI dataset confirms that the TACDAR approach is superior compared to other datasets.

Why it matters

This study proposes a deep learning method for classifying four classes of Alzheimer's disease from MRI images, reporting improved accuracy over several other methods on the ADNI-MRI dataset. It is a diagnostic classification approach and does not report any treatment, cure, or prevention.

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Study details

Effect size13.5%; 11.8%; 10.8%
Evidence strengthLow

How this study scored 30/100

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. We have no data on senior author's track record, so that was left out: it earned 29 of the 96 points we could judge it on, which is 30 out of 100.

Component	Points	Why
Journal tier	7 / 10	Tier 3 — peer-reviewed journal (International Journal of Computational Intelligence Systems)
Journal's citation record	0 / 4	International Journal of Computational Intelligence Systems: journal h-index 72 (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	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	0 / 7	Cited 0 times (OpenAlex, 2026-10-10)
Citations per year	0 / 5	0.0 citations a year over 2 year(s) since 2026
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 Marimuthu Ramya holds only 1 work(s), too few to judge a track record — left out of this score
Institution's track record	0 / 3	Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology: institution h-index 142 (OpenAlex)
Multi-centre collaboration	1 / 3	OpenAlex lists 2 institution(s) in 1 country
Indexed in PubMed	1 / 2	Not in PubMed, but has a DOI
Free to read	2 / 2	Free to read
Points	29 / 96	Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 29 of the 96 points we could judge it on is 30 out of 100.
Total	30 / 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

Geetha, N., Priya, S., Revathi, B., & Ramya, M. (2026). TACDAR: Tetra Alzheimer Disease Classification via Dilated Coordinate Attention Based RegNet. International Journal of Computational Intelligence Systems. https://doi.org/10.1007/s44196-026-01374-0

Vancouver

Geetha N, Priya S, Revathi B, Ramya M. TACDAR: Tetra Alzheimer Disease Classification via Dilated Coordinate Attention Based RegNet. International Journal of Computational Intelligence Systems. 2026. doi:10.1007/s44196-026-01374-0.

BibTeX

@article{geetha2026-28dafa2e,
  author  = {Geetha, N. and Priya, S. and Revathi, B. and Ramya, Marimuthu},
  title   = {{TACDAR: Tetra Alzheimer Disease Classification via Dilated Coordinate Attention Based RegNet}},
  journal = {International Journal of Computational Intelligence Systems},
  year    = {2026},
  doi     = {10.1007/s44196-026-01374-0},
  url     = {https://doi.org/10.1007/s44196-026-01374-0}
}

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