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ALZENET: Deep learning-based early prediction of Alzheimer's disease through magnetic resonance imaging analysis
Md Asaduzzaman, Md. Khorshed Alom, Md. Ebtidaul Karim. Telematics and Informatics Reports, 2025-02-14
Topic: Amyloid, tau and blood-test diagnosis
Alzheimer's Disease (AD) is a particular form of dementiacharacterized by a steady decline in brain function and cognitive abilities, leading to memory loss, disorientation, and trouble doing daily tasks. It generally occurs in people aged over 65, and currently, there are around 55 million Alzheimer's patients which is becoming a growing concern, and the death rate due to AD has increased significantly. Existing ways of categorizing patients include medical records, cognitive testing, and magnetic resonance imaging (MRI), which assist in diagnosing the condition, but a deep learning-based approach helps to discover Alzheimer's at an early stage. In this study, we propose an Alzheimer Recognition Ensemble Network (ALZENET) for classifying various stages of Alzheimer using MRI data. The study utilized the Kaggle MRI-based AD dataset, which comprised a total of 6,400 MRI images across four distinct classes. To address the significant class imbalance present in the dataset, the SMOTE technique was employed. Our ensemble model, which combines VGG16, Inception V3, and ResNet50 with a multi-layered CNN architecture, achieved a test accuracy of 97.31 %, significantly outperforming existing approaches in Alzheimer's disease classification. Additionally, a deep learning-based web application was developed to assist doctors in efficiently identifying different stages of Alzheimer's disease. This approach provides a promising tool for the early and accurate detection of Alzheimer's disease, potentially improving patient outcomes.
Key findings
- It generally occurs in people aged over 65, and currently, there are around 55 million Alzheimer's patients which is becoming a growing concern, and the death rate due to AD has increased significantly.
- To address the significant class imbalance present in the dataset, the SMOTE technique was employed.
- Our ensemble model, which combines VGG16, Inception V3, and ResNet50 with a multi-layered CNN architecture, achieved a test accuracy of 97.31 %, significantly outperforming existing approaches in Alzheimer's disease classification.
Why it matters
This study combined several deep learning models to classify stages of Alzheimer's disease from MRI images, achieving 97.31% test accuracy, and developed a web application for doctors. It is a detection method only and does not test any treatment, cure, or prevention.
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Study details
- Participants6,400
- Evidence strengthLow
How this study scored 48/100
Published in a peer-reviewed journal. Scored zero for study design, effect reported 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 46 of the 96 points we could judge it on, which is 48 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Telematics and Informatics Reports) |
| Journal's citation record | 0 / 4 | Telematics and Informatics Reports: journal h-index 36 (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 | 3 / 4 | 6,400 participants |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 4 / 7 | Cited 40 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 13.3 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 5 / 6 | Cited 25.4× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 1 / 6 | Cited by 2 other studies in this library |
| Still being cited | 2 / 4 | Cited 40 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 | Khulna University of Engineering and Technology: institution h-index 158 (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 | 46 / 96 | Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 46 of the 96 points we could judge it on is 48 out of 100. |
| Total | 48 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1016/j.teler.2025.100189
- Original source →
- Free full text: Telematics and Informatics Reports →
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
Asaduzzaman, M., Alom, M. K., & Karim, M. E. (2025). ALZENET: Deep learning-based early prediction of Alzheimer's disease through magnetic resonance imaging analysis. Telematics and Informatics Reports. https://doi.org/10.1016/j.teler.2025.100189
Vancouver
Asaduzzaman M, Alom MK, Karim ME. ALZENET: Deep learning-based early prediction of Alzheimer's disease through magnetic resonance imaging analysis. Telematics and Informatics Reports. 2025. doi:10.1016/j.teler.2025.100189.
BibTeX
@article{asaduzzaman2025-4f4d3a10,
author = {Asaduzzaman, Md and Alom, Md. Khorshed and Karim, Md. Ebtidaul},
title = {{ALZENET: Deep learning-based early prediction of Alzheimer's disease through magnetic resonance imaging analysis}},
journal = {Telematics and Informatics Reports},
year = {2025},
doi = {10.1016/j.teler.2025.100189},
url = {https://doi.org/10.1016/j.teler.2025.100189}
}
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