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28/100

✓ Peer-reviewed alzhub usefulness score 28 out of 100. Brain scans and imaging

Topic: Amyloid, tau and blood-test diagnosis

Alzheimer's Disease acts as a degenerative brain trauma that results in poor memory, alongside impairing mental functioning. Early detection of Alzheimer's Disease is aimed at controlling symptoms in addition to improving medical care for patients. Traditional diagnosis based on imaging procedures in conjunction with cognitive examinations takes a long time for experienced experts to analyze. Deep learning technology triggered interest in automated MRI data analysis techniques because they execute quickly and attain high accuracy levels. The application explores the development of an Alzheimer's Disease Diagnosis System as a web-based light application by using a custom Convolutional Neural Network (CNN) in its implementation. The system analyzes brain MRI scans to perform patient group assignment into Mild Demented, Moderate Demented, Very Mild Demented, and Non-Demented categories. The system shows a valid accuracy of 88.5%, prediction probabilities, and stage-dependent medical advice that generate downloadable diagnosis reports on the Streamlit platform. The system maintains user privacy through non-storage of their information and includes readable medical warning notices during use. The system is a working bridge between experimental technologies for Alzheimer's identification and available medical practices in clinical environments.

Key findings

Alzheimer's Disease acts as a degenerative brain trauma that results in poor memory, alongside impairing mental functioning.

Why it matters

This study built a web-based system using a custom convolutional neural network to sort brain MRI scans into Mild Demented, Moderate Demented, Very Mild Demented and Non-Demented categories, reporting 88.5% accuracy. It is a diagnostic classification tool only and does not test any treatment, cure or prevention.

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Study details

Effect size88.5%
Evidence strengthLow

How this study scored 28/100

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 27 of the 96 points we could judge it on, which is 28 out of 100.

Component	Points	Why
Journal tier	7 / 10	Tier 3 — peer-reviewed journal (International Research Journal on Advanced Engineering Hub (IRJAEH))
Journal's citation record	0 / 4	International Research Journal on Advanced Engineering Hub (IRJAEH): journal h-index 9 (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	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	Senior author's record not available — left out of this score
Institution's track record	0 / 3	Jawaharlal Nehru Technological University, Hyderabad: institution h-index 146 (OpenAlex)
Multi-centre collaboration	0 / 3	OpenAlex lists 1 institution(s) in 1 country — 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	27 / 96	Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 27 of the 96 points we could judge it on is 28 out of 100.
Total	28 / 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

Tangallapally, S., Ahmed, M. S., Mounika, G., Praveen, S., Revathi, M. K., & Ramesh, D. M. (2025). Early Detection of Alzheimers Disease Using Deep Learning. International Research Journal on Advanced Engineering Hub (IRJAEH). https://doi.org/10.47392/irjaeh.2025.0319

Vancouver

Tangallapally S, Ahmed MS, Mounika G, Praveen S, Revathi MK, Ramesh DM. Early Detection of Alzheimers Disease Using Deep Learning. International Research Journal on Advanced Engineering Hub (IRJAEH). 2025. doi:10.47392/irjaeh.2025.0319.

BibTeX

@article{tangallapally2025-27515499,
  author  = {Tangallapally, Sravani and Ahmed, Muhammad Saad and Mounika, Gurram and Praveen, Sabavat and Revathi, Mrs. K. and Ramesh, Dr. M.},
  title   = {{Early Detection of Alzheimers Disease Using Deep Learning}},
  journal = {International Research Journal on Advanced Engineering Hub (IRJAEH)},
  year    = {2025},
  doi     = {10.47392/irjaeh.2025.0319},
  url     = {https://doi.org/10.47392/irjaeh.2025.0319}
}

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