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Alzheimer Detection Using Deep Learning Methods
Othman Othman, Mohammed Q. Jawad, Othman Mohammed Jasim. Journal of Intelligent Systems and Internet of Things, 2025-01-01
Topic: Amyloid, tau and blood-test diagnosis
This study proposes a deep learning-based framework to detect and classify Alzheimer's disease (AD) in the early stages using medical imaging, and specifically Magnetic Resonance Imaging (MRI). Specifically, we propose a Convolution Neural Network (CNN) based model and transfer learn (MobileNet) through pre-trained models based on task domain to improve model performance on binary AD classification. Thanks to minimizing computational complexity and memory costs, the model with 99.86% accuracy rate can mitigate overfitting and is an ideal approach for real time and eco-friendly monitoring of AD evolution. The findings suggest that the model could help clinicians in diagnosing AD even based on MRI images, which has great potential as a scalable and efficient solution for the early-stage diagnosis and classification of the disease. Our work will include the addition of further pre-trained models, increased dataset size via data augmentation, and the application of MRI segmentation to better isolate some of the key features of Alzheimer.
Key findings
- The findings suggest that the model could help clinicians in diagnosing AD even based on MRI images, which has great potential as a scalable and efficient solution for the early-stage diagnosis and classification of the disease.
Why it matters
This study used a convolutional neural network and a pre-trained MobileNet model to classify MRI scans for Alzheimer's disease, reporting 99.86% accuracy. The authors suggest the model could help clinicians diagnose Alzheimer's disease from MRI images, but it is a diagnostic tool and does not test any treatment, cure or prevention.
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Study details
- Effect size99.86%
- Evidence strengthLow
How this study scored 27/100
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 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 (Journal of Intelligent Systems and Internet of Things) |
| Journal's citation record | 0 / 4 | Journal of Intelligent Systems and Internet of Things: journal h-index 18 (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 | OpenAlex's profile for last-listed author Othman Mohammed Jasim 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 | 0 / 2 | Behind a paywall — scores zero |
| 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 →
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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
Othman, O., Jawad, M. Q., & Jasim, O. M. (2025). Alzheimer Detection Using Deep Learning Methods. Journal of Intelligent Systems and Internet of Things. https://doi.org/10.54216/jisiot.160215
Vancouver
Othman O, Jawad MQ, Jasim OM. Alzheimer Detection Using Deep Learning Methods. Journal of Intelligent Systems and Internet of Things. 2025. doi:10.54216/jisiot.160215.
BibTeX
@article{othman2025-12c0518b,
author = {Othman, Othman and Jawad, Mohammed Q. and Jasim, Othman Mohammed},
title = {{Alzheimer Detection Using Deep Learning Methods}},
journal = {Journal of Intelligent Systems and Internet of Things},
year = {2025},
doi = {10.54216/jisiot.160215},
url = {https://doi.org/10.54216/jisiot.160215}
}