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AD-HOLDER: Alzheimer’s Disease Detection via Machine Learning based Histograms of Light GBM Classifier using MRI and PET Images
S Mahalakshmi, K Valarmathi. Measurement Science Review, 2025-12-01
Topic: Amyloid, tau and blood-test diagnosis
Abstract Alzheimer’s disease (AD) is an irreversible brain condition that impairs memory and cognitive processes. However, existing Alzheimer detection methods have shown low diagnostic accuracy ( ACC ) due to limited images and inefficient feature analysis. In this paper, a novel AD-HOLDER model is proposed for early recognition of AD using dual imaging methods: magnetic resonance imaging (MRI) and positron emission tomography (PET). The proposed AD-HOLDER model presents an integrated framework that uniquely combines Deep Image Prior (DIP) denoising, Histogram of Oriented Gradients (HOG) feature extraction, and Light Gradient Boosting Machine (LGBM) classification for AD detection from MRI and PET images. The HOG method aims to enhance the spatial and contextual representation of neurological patterns by combining structural features from MRI and statistical features from PET images. A classifier based on the LGBM processes the dual features to classify images as either normal or abnormal, effectively capturing complex patterns and improving classification ACC . The abnormal region is segmented using a graph-based segmentation (GBS) model to accurately detect affected areas for accurate detection of AD. The effectiveness of the proposed AD-HOLDER model is evaluated using ACC , specificity ( SPE ), precision ( PRE ), recall ( REC ), and F1-score ( F 1) based on the OASIS dataset. The proposed AD-HOLDER model achieves a classification ACC of 99.12% through machine learning. The proposed AD-HOLDER model increases overall ACC by 1.55 %, 25.44 %, and 3.14% compared to the Gradient Boosting Algorithm (GBA), Explainable Artificial Intelligence (XAI), and Computer-Aided Diagnosis (CAD) systems, respectively.
Key findings
- The proposed AD-HOLDER model increases overall ACC by 1.55 %, 25.44 %, and 3.14% compared to the Gradient Boosting Algorithm (GBA), Explainable Artificial Intelligence (XAI), and Computer-Aided Diagnosis (CAD) systems, respectively.
Why it matters
This study proposed a machine learning model combining MRI and PET images to detect Alzheimer's disease, reporting improved accuracy compared to other methods. It is a detection method only and does not test any treatment, cure, or prevention.
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Study details
- Effect size99.12%; 3.14%
- Evidence strengthLow
How this study scored 31/100
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 29 of the 93 points we could judge it on, which is 31 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Measurement Science Review) |
| Journal's citation record | 0 / 4 | Measurement Science Review: journal h-index 35 (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 | 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 K Valarmathi 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 | 2 / 2 | Free to read |
| Points | 29 / 93 | Scored on 18 of 20 parts. We have no data for the rest, so they are left out: 29 of the 93 points we could judge it on is 31 out of 100. |
| Total | 31 / 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
Mahalakshmi, S., & Valarmathi, K. (2025). AD-HOLDER: Alzheimer’s Disease Detection via Machine Learning based Histograms of Light GBM Classifier using MRI and PET Images. Measurement Science Review. https://doi.org/10.2478/msr-2025-0043
Vancouver
Mahalakshmi S, Valarmathi K. AD-HOLDER: Alzheimer’s Disease Detection via Machine Learning based Histograms of Light GBM Classifier using MRI and PET Images. Measurement Science Review. 2025. doi:10.2478/msr-2025-0043.
BibTeX
@article{mahalakshmi2025-94264a9d,
author = {Mahalakshmi, S and Valarmathi, K},
title = {{AD-HOLDER: Alzheimer’s Disease Detection via Machine Learning based Histograms of Light GBM Classifier using MRI and PET Images}},
journal = {Measurement Science Review},
year = {2025},
doi = {10.2478/msr-2025-0043},
url = {https://doi.org/10.2478/msr-2025-0043}
}
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