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Detection and Classification of Alzheimer’s Disease Using Deep and Machine Learning
Muhammad Zaeem Khalid, Nida Iqbal, Babar Ali, Jawwad Sami Ur Rahman, Saman Iqbal, Lama Almudaimeegh, Zuhal Y. Hamd, Awadia Gareeballah. Tomography, 2025-12-26
Topic: Amyloid, tau and blood-test diagnosis
Random Forest achieves the highest accuracy of 97% on clinical data, while CNN achieves the best overall performance of 94% in MRI-based staging. SHAP and Grad-CAM were used to find clinically relevant characteristics and brain areas, including hippocampal atrophy and ventricular enlargement. Integrating clinical and imaging data and interpretable AI improves the accuracy and reliability of AD staging. The proposed model offers a valid and clear diagnostic route, which can assist clinicians in making timely diagnoses and adjusting individual treatment.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Khalid, M. Z., Iqbal, N., Ali, B., Rahman, J. S. U., Iqbal, S., Almudaimeegh, L., et al. (2025). Detection and Classification of Alzheimer’s Disease Using Deep and Machine Learning. Tomography. https://doi.org/10.3390/tomography12010004
Vancouver
Khalid MZ, Iqbal N, Ali B, Rahman JSU, Iqbal S, Almudaimeegh L, et al. Detection and Classification of Alzheimer’s Disease Using Deep and Machine Learning. Tomography. 2025. doi:10.3390/tomography12010004. PMID: 41591137.
BibTeX
@article{khalid2025-41591137,
author = {Khalid, Muhammad Zaeem and Iqbal, Nida and Ali, Babar and Rahman, Jawwad Sami Ur and Iqbal, Saman and Almudaimeegh, Lama and Hamd, Zuhal Y. and Gareeballah, Awadia},
title = {{Detection and Classification of Alzheimer’s Disease Using Deep and Machine Learning}},
journal = {Tomography},
year = {2025},
doi = {10.3390/tomography12010004},
pmid = {41591137},
url = {https://doi.org/10.3390/tomography12010004}
}
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. The record names 8 authors; APA and Vancouver show the first six, BibTeX and the downloads carry all of them. Check the citation against the original before you use it.
Key findings
- Random Forest achieves the highest accuracy of 97% on clinical data, while CNN achieves the best overall performance of 94% in MRI-based staging.
- SHAP and Grad-CAM were used to find clinically relevant characteristics and brain areas, including hippocampal atrophy and ventricular enlargement.
- Integrating clinical and imaging data and interpretable AI improves the accuracy and reliability of AD staging.
- The proposed model offers a valid and clear diagnostic route, which can assist clinicians in making timely diagnoses and adjusting individual treatment.
Why it matters
This study reports that a Random Forest model reached 97% accuracy on clinical data and a CNN reached 94% in MRI-based staging of Alzheimer's disease, with interpretable AI highlighting hippocampal atrophy and ventricular enlargement. It is a diagnostic study and does not test any treatment, cure or prevention.
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Study details
- Evidence strengthLow
How this study scored 36/100
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.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Tomography) |
| Journal's citation record | 0 / 4 | Tomography: journal h-index 43 (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 | 2 / 7 | Cited 5 times (OpenAlex, 2026-10-10) |
| Citations per year | 1 / 5 | 1.7 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 3 / 6 | Cited 3.2× 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 | 1 / 4 | Cited 5 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 1 / 4 | Last-listed author Awadia Gareeballah: h-index 10 (OpenAlex, whole career) |
| Institution's track record | 0 / 3 | University of the Punjab: institution h-index 250 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 6 institution(s) in 2 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 41591137) |
| Free to read | 2 / 2 | Free to read |
| Total | 36 / 100 | — |
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