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31/100

✓ Peer-reviewed alzhub usefulness score 31 out of 100. Brain scans and imaging

Topic: Amyloid, tau and blood-test diagnosis

Alzheimer's disease (AD) is a memory and cognitive function neurodegenerative disease. Early diagnosis is important for appropriate treatment, but conventional diagnosing techniques are not very effective. In this paper, automatic detection of Alzheimer's disease from MRI scans using deep learning techniques is discussed. We trained and compared six pre-trained Convolutional Neural Network (CNN) models—VGG19, ResNet101, EfficientNetB3, MobileNetV2, InceptionV3, and DenseNet121—to differentiate MRI scans as Alzheimer and Non-Alzheimer. In experiments, InceptionV3 is the optimal model for classification of Alzheimer's disease and all others are inferior with an accuracy of 99.2%. Confusion matrices, classification reports, and accuracy metrics were utilized to test performance. We also created a web-based real-time MRI classification tool with a user-friendly interface for clinicians. This work showcases the potential of deep learning in medical imaging and the detection of early disease, and opens the door to AI-augmented improvement in Alzheimer's diagnosis

Cite this study APA, Vancouver, BibTeX · download .bib or .ris

APA 7th

Patel, V. (2025). AI-Driven Alzheimer’s Detection Performance Analysis of Pretrained CNN Models on MRI Data. International Journal for Research in Applied Science and Engineering Technology. https://doi.org/10.22214/ijraset.2025.68354

Vancouver

Patel V. AI-Driven Alzheimer’s Detection Performance Analysis of Pretrained CNN Models on MRI Data. International Journal for Research in Applied Science and Engineering Technology. 2025. doi:10.22214/ijraset.2025.68354.

BibTeX

@article{patel2025-90032e72,
  author  = {Patel, Varun},
  title   = {{AI-Driven Alzheimer’s Detection Performance Analysis of Pretrained CNN Models on MRI Data}},
  journal = {International Journal for Research in Applied Science and Engineering Technology},
  year    = {2025},
  doi     = {10.22214/ijraset.2025.68354},
  url     = {https://doi.org/10.22214/ijraset.2025.68354}
}

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Key findings

We trained and compared six pre-trained Convolutional Neural Network (CNN) models—VGG19, ResNet101, EfficientNetB3, MobileNetV2, InceptionV3, and DenseNet121—to differentiate MRI scans as Alzheimer and Non-Alzheimer.
In experiments, InceptionV3 is the optimal model for classification of Alzheimer's disease and all others are inferior with an accuracy of 99.2%.
Confusion matrices, classification reports, and accuracy metrics were utilized to test performance.
We also created a web-based real-time MRI classification tool with a user-friendly interface for clinicians.
This work showcases the potential of deep learning in medical imaging and the detection of early disease, and opens the door to AI-augmented improvement in Alzheimer's diagnosis

Why it matters

This study compared six pre-trained deep learning models for detecting Alzheimer's disease from MRI scans, with one model reaching 99.2% accuracy, and also created a web tool for clinicians. It is a detection method only and does not test any treatment, cure, or prevention.

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Study details

Effect size99.2%
Evidence strengthLow

How this study scored 31/100

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 and institution's track record, so those were left out: it earned 29 of the 93 points we could judge it on, which is 31 out of 100.

Component	Points	Why
Journal tier	7 / 10	Tier 3 — peer-reviewed journal (International Journal for Research in Applied Science and Engineering Technology)
Journal's citation record	0 / 4	International Journal for Research in Applied Science and Engineering Technology: journal h-index 42 (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	1 / 7	Cited 1 times (OpenAlex, 2026-10-10)
Citations per year	0 / 5	0.3 citations a year over 3 year(s) since 2025
Impact for its field and year	1 / 6	Cited 0.6× 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 1 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	— / 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	29 / 93	Scored on 18 of 20 parts. We have no data for the rest, so they are left out: 29 of the 93 points we could judge it on is 31 out of 100.
Total	31 / 100	—

What each of these 20 components measures, and what this score is not →

Read the original

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