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A Deep Siamese Convolution Neural Network for Multi-Class Classification of Alzheimer Disease
Mehmood A, Maqsood M, Bashir M, Shuyuan Y.. Brain sciences, 2020-02-05
Topic: Amyloid, tau and blood-test diagnosis
Alzheimer's disease (AD) may cause damage to the memory cells permanently, which results in the form of dementia. The diagnosis of Alzheimer's disease at an early stage is a problematic task for researchers. For this, machine learning and deep convolutional neural network (CNN) based approaches are readily available to solve various problems related to brain image data analysis. In clinical research, magnetic resonance imaging (MRI) is used to diagnose AD. For accurate classification of dementia stages, we need highly discriminative features obtained from MRI images. Recently advanced deep CNN-based models successfully proved their accuracy. However, due to a smaller number of image samples available in the datasets, there exist problems of over-fitting hindering the performance of deep learning approaches. In this research, we developed a Siamese convolutional neural network (SCNN) model inspired by VGG-16 (also called Oxford Net) to classify dementia stages. In our approach, we extend the insufficient and imbalanced data by using augmentation approaches. Experiments are performed on a publicly available dataset open access series of imaging studies (OASIS), by using the proposed approach, an excellent test accuracy of 99.05% is achieved for the classification of dementia stages. We compared our model with the state-of-the-art models and discovered that the proposed model outperformed the state-of-the-art models in terms of performance, efficiency, and accuracy.
Key findings
- Alzheimer's disease (AD) may cause damage to the memory cells permanently, which results in the form of dementia.
Why it matters
This study developed a Siamese convolutional neural network to classify dementia stages from MRI images, reporting 99.05% test accuracy on a publicly available dataset. It is a machine learning method for classifying dementia stages, not a treatment, cure or prevention for Alzheimer's disease.
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Study details
- Effect size99.05%
- Evidence strengthLow
How this study scored 49/100
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 47 of the 96 points we could judge it on, which is 49 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Brain sciences) |
| Journal's citation record | 1 / 4 | Brain Sciences: journal h-index 115 (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 | 2 / 5 | 2020 — within 10 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 | 5 / 7 | Cited 263 times (OpenAlex, 2026-10-10) |
| Citations per year | 4 / 5 | 32.9 citations a year over 8 year(s) since 2020 |
| Impact for its field and year | 5 / 6 | Cited 24.2× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 2 / 6 | Cited by 5 other studies in this library |
| Still being cited | 3 / 4 | Cited 112 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 | Xidian University: institution h-index 386 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 3 institution(s) in 2 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 32033462) |
| Free to read | 2 / 2 | Free to read |
| Points | 47 / 96 | Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 47 of the 96 points we could judge it on is 49 out of 100. |
| Total | 49 / 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
Mehmood, A., Maqsood, M., Bashir, M., & Shuyuan, Y. (2020). A Deep Siamese Convolution Neural Network for Multi-Class Classification of Alzheimer Disease. Brain sciences. https://doi.org/10.3390/brainsci10020084
Vancouver
Mehmood A, Maqsood M, Bashir M, Shuyuan Y. A Deep Siamese Convolution Neural Network for Multi-Class Classification of Alzheimer Disease. Brain sciences. 2020. doi:10.3390/brainsci10020084. PMID: 32033462.
BibTeX
@article{mehmood2020-32033462,
author = {Mehmood, A. and Maqsood, M. and Bashir, M. and Shuyuan, Y.},
title = {{A Deep Siamese Convolution Neural Network for Multi-Class Classification of Alzheimer Disease}},
journal = {Brain sciences},
year = {2020},
doi = {10.3390/brainsci10020084},
pmid = {32033462},
url = {https://doi.org/10.3390/brainsci10020084}
}
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