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29/100

✓ Peer-reviewed alzhub usefulness score 29 out of 100. Brain scans and imaging

Topic: Amyloid, tau and blood-test diagnosis

Bilgisayar destekli cihaz ve sistemlerin sağlık sektöründe giderek artan kullanımı, hastalıkların daha erken ve daha hızlı teşhis edilmesine olanak sağlamaktadır. Bilgisayar destekli sistem ve cihazlar özellikle nörolojik hastalıkların tanı ve görüntülemesinde kritik bir rol oynamaktadır. Merkezi sinir sistemi ve bilişsel fonksiyonların değerlendirilmesindeki gelişmeler, özellikle Alzheimer Hastalığı gibi nörodejeneratif hastalıkların erken teşhisinde önemli avantajlar sağlamaktadır. Çalışmada kullanılan veriler Kaggle platformunda açık kaynaklı olarak bulunan “Augmented Alzheimer MRI Dataset” isimli veri setinden alınmıştır. Bu veri setinde ikinci klasör kullanılmıştır. Toplamda 33.894 adet görüntü bulunmaktadır. Çalışmada Alzheimer hastalığı teşhisi analizi için derin öğrenme sınıflandırıcı model olarak Convolutional Neural Network (CNN) kullanılmıştır. Performans analizi yapılmıştır. Kullanılan CNN model accuracy değerini 0.9102 olarak vermiştir. Bu sonuçlar, CNN modelin kullanılmasıyla veri kaybının düşük olacağını, performansın iyi olacağını göstermektedir.

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

APA 7th

Gündüzalp, C., Tulum, G., & Gündüzalp, T. (2025). Alzheimer Hastalığının Tespiti CNN Model Sınıflandırması. International Journal of Pure and Applied Sciences. https://doi.org/10.29132/ijpas.1582591

Vancouver

Gündüzalp C, Tulum G, Gündüzalp T. Alzheimer Hastalığının Tespiti CNN Model Sınıflandırması. International Journal of Pure and Applied Sciences. 2025. doi:10.29132/ijpas.1582591.

BibTeX

@article{gunduzalp2025-3e76fe4e,
  author  = {Gündüzalp, Ceren and Tulum, Gökalp and Gündüzalp, Tahsin},
  title   = {{Alzheimer Hastalığının Tespiti CNN Model Sınıflandırması}},
  journal = {International Journal of Pure and Applied Sciences},
  year    = {2025},
  doi     = {10.29132/ijpas.1582591},
  url     = {https://doi.org/10.29132/ijpas.1582591}
}

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.

Key findings

Merkezi sinir sistemi ve bilişsel fonksiyonların değerlendirilmesindeki gelişmeler, özellikle Alzheimer Hastalığı gibi nörodejeneratif hastalıkların erken teşhisinde önemli avantajlar sağlamaktadır. Çalışmada kullanılan veriler Kaggle platformunda açık kaynaklı olarak bulunan “Augmented Alzheimer MRI Dataset” isimli veri setinden alınmıştır.
Bu veri setinde ikinci klasör kullanılmıştır.
Toplamda 33.894 adet görüntü bulunmaktadır. Çalışmada Alzheimer hastalığı teşhisi analizi için derin öğrenme sınıflandırıcı model olarak Convolutional Neural Network (CNN) kullanılmıştır.
Kullanılan CNN model accuracy değerini 0.9102 olarak vermiştir.
Bu sonuçlar, CNN modelin kullanılmasıyla veri kaybının düşük olacağını, performansın iyi olacağını göstermektedir.

Why it matters

This study used a deep learning model called a Convolutional Neural Network to classify Alzheimer's disease from MRI images, achieving an accuracy of 0.9102. It is a computer-based detection method and does not test any treatment, cure, or prevention for Alzheimer's disease.

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Study details

Evidence strengthLow

How this study scored 29/100

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, so that was left out: it earned 28 of the 96 points we could judge it on, which is 29 out of 100.

Component	Points	Why
Journal tier	7 / 10	Tier 3 — peer-reviewed journal (International Journal of Pure and Applied Sciences)
Journal's citation record	0 / 4	International Journal of Pure and Applied Sciences: journal h-index 10 (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	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	OpenAlex's profile for last-listed author Tahsin Gündüzalp holds only 1 work(s), too few to judge a track record — left out of this score
Institution's track record	0 / 3	Sağlık Bilimleri Üniversitesi: institution h-index 161 (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	28 / 96	Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 28 of the 96 points we could judge it on is 29 out of 100.
Total	29 / 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).

Studies this one cites

Referenced by this study and also in the alzhub library, so you can read them here.

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