Email this article 
(Parahyangan Catholic University, Indonesia)(2) Risti Suryantari
(Parahyangan Catholic University, Indonesia)(3) Janto V. Sulungbudi
(Parahyangan Catholic University, Indonesia)*corresponding author
AbstractAlzheimer’s disease is a progressive neurodegenerative disorder that leads to a gradual decline in memory and cognitive function, most commonly affecting individuals over the age of 65. Early detection is essential to enable timely interventions, slow disease progression, and improve quality of life. This study aimed to identify the most dominant texture features from brain MRI images using the Gray Level Co-occurrence Matrix (GLCM) for feature extraction. The extracted features were analyzed through non-parametric statistical tests and machine learning algorithms, including Decision Tree and Random Forest, and validated with cross-validation procedures to ensure robustness. The findings revealed that contrast at 90° consistently emerged as the most significant feature, capturing vertical texture variations associated with brain atrophy, while correlation at 135° provided additional discriminatory power by representing disrupted pixel intensity relationships. In combination, these features enhanced the accuracy of classification models, outperforming other GLCM parameters. The results emphasize that careful selection of texture features improves both accuracy and stability in distinguishing between Alzheimer’s and non-Alzheimer’s brains. This study demonstrates that image-based machine learning frameworks can serve as reliable tools to support early detection of Alzheimer’s disease, offering valuable implications for clinical practice and guiding future research on efficient, non-invasive diagnostic approaches.
KeywordsAlzheimer; Contrast; Correlation; GLCM; Machine learning
|
DOIhttps://doi.org/10.29099/ijair.v9i1.1.1509 |
Article metrics10.29099/ijair.v9i1.1.1509 Abstract views : 122 | PDF views : 2 |
Cite |
Full Text Download
|
References
P. Scheltens et al., “Alzheimer’s disease,” Apr. 24, 2021, Elsevier B.V. doi: 10.1016/S0140-6736(20)32205-4.
W. Hao and A. Friedman, “Mathematical model on Alzheimer’s disease,” BMC Syst Biol, vol. 10, no. 1, Nov. 2016, doi: 10.1186/s12918-016-0348-2.
S. Lee, H. Lee, and K. W. Kim, “Magnetic resonance imaging texture predicts progression to dementia due to Alzheimer disease earlier than hippocampal volume,” Journal of Psychiatry and Neuroscience, vol. 45, no. 1, pp. 7–14, 2020, doi: 10.1503/jpn.180171.
J. Cummings and S. Salloway, “Aducanumab: Appropriate use recommendations,” Mar. 01, 2022, NLM (Medline). doi: 10.1002/alz.12444.
S. I. A. Cohen, M. Vendruscolo, C. M. Dobson, and T. P. J. Knowles, “From macroscopic measurements to microscopic mechanisms of protein aggregation,” Aug. 10, 2012, Academic Press. doi: 10.1016/j.jmb.2012.02.031.
M. A. Deture and D. W. Dickson, “The neuropathological diagnosis of Alzheimer’s disease,” Aug. 02, 2019, BioMed Central Ltd. doi: 10.1186/s13024-019-0333-5.
Mayo Clinic, “Alzheimer’s disease,” Aug. 2024. Accessed: Jun. 11, 2025. [Online]. Available: https://www.mayoclinic.org/diseases-conditions/alzheimers-disease/symptoms-causes/syc-20350447
D. Nutt, “MRI scans reveal brain shrinkage in Alzheimer’s disease patients,” Imperial College London.
F. T. Kurniati, I. Sembiring, A. Setiawan, I. Setyawan, and R. R. Huizen, “GLCM-Based Feature Combination for Extraction Model Optimization in Object Detection Using Machine Learning,” Jurnal Ilmiah Teknik Elektro Komputer dan Informatika, vol. 9, no. 4, pp. 1196–1205, Jan. 2024, doi: 10.26555/jiteki.v9i4.27842.
S. S. Bamber and T. Vishvakarma, “Medical image classification for Alzheimer’s using a deep learning approach,” Journal of Engineering and Applied Science, vol. 70, no. 1, Dec. 2023, doi: 10.1186/s44147-023-00211-x.
L. Sørensen et al., “Early detection of Alzheimer’s disease using MRI hippocampal texture,” Hum Brain Mapp, vol. 37, no. 3, pp. 1148–1161, Mar. 2016, doi: 10.1002/hbm.23091.
A. Khalid, E. M. Senan, K. Al-Wagih, M. M. Ali Al-Azzam, and Z. M. Alkhraisha, “Automatic Analysis of MRI Images for Early Prediction of Alzheimer’s Disease Stages Based on Hybrid Features of CNN and Handcrafted Features,” Diagnostics, vol. 13, no. 9, May 2023, doi: 10.3390/diagnostics13091654.
X. Long, L. Chen, C. Jiang, and L. Zhang, “Prediction and classification of Alzheimer disease based on quantification of MRI deformation,” PLoS One, vol. 12, no. 3, Mar. 2017, doi: 10.1371/journal.pone.0173372.
A. Sarica, A. Cerasa, and A. Quattrone, “Random forest algorithm for the classification of neuroimaging data in Alzheimer’s disease: A systematic review,” Oct. 06, 2017, Frontiers Media S.A. doi: 10.3389/fnagi.2017.00329.
D. Selvathi and T. Emala, “MRI brain pattern analysis for detection of Alzheimer’s disease using random forest classifier,” Intelligent Decision Technologies, vol. 10, no. 4, pp. 331–340, 2016, doi: 10.3233/IDT-160260.
S. Gao, “Gray level co-occurrence matrix and extreme learning machine for Alzheimer’s disease diagnosis,” International Journal of Cognitive Computing in Engineering, vol. 2, pp. 116–129, Jun. 2021, doi: 10.1016/j.ijcce.2021.08.002.
L. S. Gill, J. Kaur, and N. Goel, “Machine learning and texture features based approach for classifying Alzheimer’s disease,” in Procedia Computer Science, Elsevier B.V., 2024, pp. 2741–2748. doi: 10.1016/j.procs.2024.04.258.
J. Samper-González et al., “Reproducible evaluation of classification methods in Alzheimer’s disease: framework and application to MRI and PET data,” 2018. [Online]. Available: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdfwww.aibl.csiro.au.://www.elsevier.com/open-access/userlicense/1.0/VersionofRecord:https://www.sciencedirect.com/science/article/pii/S1053811918307407
N. Nachar, “The Mann-Whitney U: A Test for Assessing Whether Two Independent Samples Come from the Same Distribution,” 2008.
M. J. Oliveira, P. Ribeiro, and P. M. Rodrigues, “Machine Learning-Driven GLCM Analysis of Structural MRI for Alzheimer’s Disease Diagnosis,” Bioengineering, vol. 11, no. 11, Nov. 2024, doi: 10.3390/bioengineering11111153.
J. H. Cai et al., “Magnetic Resonance Texture Analysis in Alzheimer’s disease,” Dec. 01, 2020, Elsevier Inc. doi: 10.1016/j.acra.2020.01.006.
R. A. Saputra et al., “Detecting Alzheimer’s Disease by the Decision Tree Methods Based on Particle Swarm Optimization,” in Journal of Physics: Conference Series, IOP Publishing Ltd, Nov. 2020. doi: 10.1088/1742-6596/1641/1/012025.
K. Nagawa et al., “Texture analysis of muscle MRI: machine learning-based classifications in idiopathic inflammatory myopathies,” Sci Rep, vol. 11, no. 1, Dec. 2021, doi: 10.1038/s41598-021-89311-3.
S. Salunkhe et al., “Classification of alzheimer’s disease patients using texture analysis and machine learning,” Applied System Innovation, vol. 4, no. 3, Sep. 2021, doi: 10.3390/asi4030049.
T. Kam Ho, “Random Decision Forests,” Canada, Aug. 1995.
N. Vyas, K. Modi, S. Khanna, C. Katpatal, and M. Bachute, “Bibliometric review on Classification of Alzheimer’s Disease Bibliometric review on Classification of Alzheimer’s Disease Bibliometric review of Classification of Alzheimer’s Disease.” [Online]. Available: https://digitalcommons.unl.edu/libphilprac
C. C. Luk et al., “Alzheimer’s disease: 3-Dimensional MRI texture for prediction of conversion from mild cognitive impairment,” Alzheimer’s and Dementia: Diagnosis, Assessment and Disease Monitoring, vol. 10, pp. 755–763, Jan. 2018, doi: 10.1016/j.dadm.2018.09.002.
F. Feng et al., “Radiomic features of hippocampal subregions in Alzheimer’s disease and amnestic mild cognitive impairment,” Front Aging Neurosci, vol. 10, no. SEP, Sep. 2018, doi: 10.3389/fnagi.2018.00290.
S. Lee and K. W. Kim, “Associations between texture of T1-weighted magnetic resonance imaging and radiographic pathologies in Alzheimer’s disease,” Eur J Neurol, vol. 28, no. 3, pp. 735–744, Mar. 2021, doi: 10.1111/ene.14609.
L. Wang et al., “Textural features reflecting local activity of the hippocampus improve the diagnosis of Alzheimer’s disease and amnestic mild cognitive impairment: A radiomics study based on functional magnetic resonance imaging,” Front Neurosci, vol. 16, Aug. 2022, doi: 10.3389/fnins.2022.970245.
S. Leandrou, D. Lamnisos, I. Mamais, P. A. Kyriacou, and C. S. Pattichis, “Assessment of Alzheimer’s Disease Based on Texture Analysis of the Entorhinal Cortex,” Front Aging Neurosci, vol. 12, Jul. 2020, doi: 10.3389/fnagi.2020.00176.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
________________________________________________________
The International Journal of Artificial Intelligence Research
Organized by: Prodi Teknik Informatika Fakultas Teknologi Bisnis dan Sains
Published by: Universitas Dharma Wacana
Jl. Kenanga No. 03 Mulyojati 16C Metro Barat Kota Metro Lampung
Email: jurnal.ijair@gmail.com
This work is licensed under Creative Commons Attribution-ShareAlike 4.0 International License.