Identifying Alzheimer Disease Dementia Levels Using Machine Learning Methods
Main Article Content
Abstract
Dementia, a prevalent neurodegenerative condition, is a significant aspect of Alzheimer's disease (AD), causing progressive impairment in daily functioning. Accurate and timely classification of AD stages is crucial for effective management. Machine learning and deep learning models have shown promise in this domain. In this study, we proposed an approach that utilizes support vector machine (SVM), random forest (RF), and convolutional neural network (CNN) algorithms to classify the four stages of dementia. We augmented these algorithms with watershed segmentation to extract meaningful features from MRI images. Notably, our findings demonstrate that SVM with watershed features achieves an impressive accuracy of 96.25%, outperforming other classification methods. The evaluation of our method was performed using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, and the inclusion of watershed segmentation was found to significantly enhance the performance of the models. This study highlights the potential of incorporating watershed segmentation into machine learning-based AD classification systems, offering a promising avenue for accurate diagnosis and treatment planning.
Keywords:
Alzheimer's Disease, Dementia, CNN, Random Forest, SVM, MRI image, Computer-Aided Diagnostic
Article Details
How to Cite
HUSSAIN, Md Gulzar; SHIREN, Ye.
Identifying Alzheimer Disease Dementia Levels Using Machine Learning Methods.
Medical Research Archives, [S.l.], v. 11, n. 7.1, july 2023.
ISSN 2375-1924.
Available at: <https://esmed.org/MRA/mra/article/view/4039>. Date accessed: 16 may 2024.
doi: https://doi.org/10.18103/mra.v11i7.1.4039.
Section
Research Articles
The Medical Research Archives grants authors the right to publish and reproduce the unrevised contribution in whole or in part at any time and in any form for any scholarly non-commercial purpose with the condition that all publications of the contribution include a full citation to the journal as published by the Medical Research Archives.
References
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37. Bidani A, Gouider MS, Travieso-González CM. Dementia Detection and Classification from MRI Images Using Deep Neural Networks and Transfer Learning. In: Rojas I, Joya G, Catala A, eds. Advances in Computational Intelligence. Vol 11506. Lecture Notes in Computer Science. Springer International Publishing; 2019:925-933. doi:10.1007/978-3-030-20521-8_75
2. Li X, Feng X, Sun X, Hou N, Han F, Liu Y. Global, regional, and national burden of Alzheimer’s disease and other dementias, 1990–2019. Front Aging Neurosci. 2022; 14:937486. doi:10.3389/fnagi.2022.937486
3. Castellazzi G, Cuzzoni MG, Cotta Ramusino M, et al. A Machine Learning Approach for the Differential Diagnosis of Alzheimer and Vascular Dementia Fed by MRI Selected Features. Front Neuroinformatics. 2020;14:25. doi:10.3389/fninf.2020.00025
4. Odusami M, Maskeliūnas R, Damaševičius R, Krilavičius T. Analysis of Features of Alzheimer’s Disease: Detection of Early Stage from Functional Brain Changes in Magnetic Resonance Images Using a Finetuned ResNet18 Network. Diagnostics. 2021;11(6): 1071. doi:10.3390/diagnostics11061071
5. Developer. Role of MRIs in Detecting Alzheimer’s. Envision Radiology. Published July 8, 2020. Accessed April 28, 2023. https://www.envrad.com/role-of-mris-in-detecting-alzheimers/
6. Liu S, Liu S, Cai W, Pujol S, Kikinis R, Feng D. Early diagnosis of Alzheimer’s disease with deep learning. In: 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI). IEEE; 2014:1015-1018. doi:10.1109/ISBI.2014.6868045
7. Tanveer M, Richhariya B, Khan RU, et al. Machine Learning Techniques for the Diagnosis of Alzheimer’s Disease: A Review. ACM Trans Multimed Comput Commun Appl. 2020;16(1s):1-35. doi:10.1145/3344998
8. Wen J, Thibeau-Sutre E, Diaz-Melo M, et al. Convolutional neural networks for classification of Alzheimer’s disease: Overview and reproducible evaluation. Med Image Anal. 2020;63:101694. doi:10.1016/j.media.2020.101694
9. AlSaeed D, Omar SF. Brain MRI Analysis for Alzheimer’s Disease Diagnosis Using CNN-Based Feature Extraction and Machine Learning. Sensors. 2022;22(8):2911. doi:10.3390/s22082911
10. Kamal MdS, Northcote A, Chowdhury L, Dey N, Crespo RG, Herrera-Viedma E. Alzheimer’s Patient Analysis Using Image and Gene Expression Data and Explainable-AI to Present Associated Genes. IEEE Trans Instrum Meas. 2021;70:1-7. doi:10.1109/TIM.2021.3107056
11. Chagué P, Marro B, Fadili S, et al. Radiological classification of dementia from anatomical MRI assisted by machine learning-derived maps. J Neuroradiol. 2021;48(6):412-418. doi:10.1016/j.neurad.2020.04.004
12. Bharanidharan N, Rajaguru H. Improved chicken swarm optimization to classify dementia MRI images using a novel controlled randomness optimization algorithm. Int J Imaging Syst Technol. 2020; 30(3):605-620. doi:10.1002/ima.22402
13. Haque S, Thaneeghaivel R, Gangwar M, Singh S. A Deep Learning Model in the Detection of Alzheimer Disease. Turk J Comput Math Educ. 2021;12(10):4013-4022. doi: https://doi.org/10.17762/turcomat.v12i10.5113
14. Hazarika RA, Kandar D, Maji AK. An experimental analysis of different Deep Learning based Models for Alzheimer’s Disease classification using Brain Magnetic Resonance Images. J King Saud Univ - Comput Inf Sci. 2022;34(10):8576-8598. doi:10.1016/j.jksuci.2021.09.003
15. AbdulAzeem Y, Bahgat WM, Badawy M. A CNN based framework for classification of Alzheimer’s disease. Neural Comput Appl. 2021;33(16):10415-10428. doi:10.1007/s00521-021-05799-w
16. Suganthe RC, Latha RS, Geetha M, Sreekanth GR. Diagnosis of Alzheimer’s Disease from Brain Magnetic Resonance Imaging Images using Deep Learning Algorithms. Adv Electr Comput Eng. 2020; 20(3):57-64. doi:10.4316/AECE.2020.03007
17. Murugan S, Venkatesan C, Sumithra MG, et al. DEMNET: A Deep Learning Model for Early Diagnosis of Alzheimer Diseases and Dementia From MR Images. IEEE Access. 2021;9:90319-90329. doi:10.1109/ACCESS.2021.3090474
18. Miltiadous A, Tzimourta KD, Giannakeas N, et al. Alzheimer’s Disease and Frontotemporal Dementia: A Robust Classification Method of EEG Signals and a Comparison of Validation Methods. Diagnostics. 2021;11(8):1437. doi:10.3390/diagnostics11081437
19. Hu J, Qing Z, Liu R, et al. Deep Learning-Based Classification and Voxel-Based Visualization of Frontotemporal Dementia and Alzheimer’s Disease. Front Neurosci. 2021; 14:626154. doi:10.3389/fnins.2020.626154
20. Ieracitano C, Mammone N, Bramanti A, Hussain A, Morabito FC. A Convolutional Neural Network approach for classification of dementia stages based on 2D-spectral representation of EEG recordings. Neurocomputing. 2019;323:96-107. doi:10.1016/j.neucom.2018.09.071
21. Chandaran SR, Muthusamy G, Sevalaiappan LR, Senthilkumaran N. Deep Learning-based Transfer Learning Model in Diagnosis of Diseases with Brain Magnetic Resonance Imaging. Acta Polytech Hung. 2022;19(5):127-147. doi:10.12700/APH.19.5.2022.5.7
22. Alzheimer MRI Preprocessed Dataset. Accessed April 28, 2023. https://www.kaggle.com/datasets/sachinkumar413/alzheimer-mri-dataset
23. Jack CR, Bernstein MA, Fox NC, et al. The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods. J Magn Reson Imaging. 2008;27(4):685-691. doi:10.1002/jmri.21049
24. Kornilov A, Safonov I, Yakimchuk I. A Review of Watershed Implementations for Segmentation of Volumetric Images. J Imaging. 2022;8(5):127. doi:10.3390/jimaging8050127
25. Shaik F, Prasad MNG. Feature Extraction from Micrograph Images Using Watershed Segmentation Approach. Int J Appl Eng Res. 2012;5(23-24):3665-3674.
26. Zhang W, Jiang D. The marker-based watershed segmentation algorithm of ore image. In: 2011 IEEE 3rd International Conference on Communication Software and Networks. IEEE; 2011:472-474. doi:10.1109/ICCSN.2011.6014611
27. Sarica A, Cerasa A, Quattrone A. Random Forest Algorithm for the Classification of Neuroimaging Data in Alzheimer’s Disease: A Systematic Review. Front Aging Neurosci. 2017;9:329. doi:10.3389/fnagi.2017.00329
28. Sheykhmousa M, Mahdianpari M, Ghanbari H, Mohammadimanesh F, Ghamisi P, Homayouni S. Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review. IEEE J Sel Top Appl Earth Obs Remote Sens. 2020;13:6308-6325. doi:10.1109/JSTARS.2020.3026724
29. Chaganti SY, Nanda I, Pandi KR, Prudhvith TGNRSN, Kumar N. Image Classification using SVM and CNN. In: 2020 International Conference on Computer Science, Engineering and Applications (ICCSEA). IEEE; 2020:1-5. doi:10.1109/ICCSEA49143.2020.9132851
30. De Silva K, Kunz H. Prediction of Alzheimer’s disease from magnetic resonance imaging using a convolutional neural network. Intell-Based Med. 2023;7:100091. doi:10.1016/j.ibmed.2023.100091
31. Bharanidharan N, Harikumar R. Modified Grey Wolf Randomized Optimization in Dementia Classification Using MRI Images. IETE J Res. 2022;68(4):2531-2540. doi:10.1080/03772063.2020.1715852
32. Bharati S, Podder P, Thanh DNH, Prasath VBS. Dementia classification using MR imaging and clinical data with voting based machine learning models. Multimed Tools Appl. 2022;81(18):25971-25992. doi:10.1007/s11042-022-12754-x
33. Herzog NJ, Magoulas GD. Brain Asymmetry Detection and Machine Learning Classification for Diagnosis of Early Dementia. Sensors. 2021;21(3):778. doi:10.3390/s21030778
34. Mohammed BA, Senan EM, Rassem TH, et al. Multi-Method Analysis of Medical Records and MRI Images for Early Diagnosis of Dementia and Alzheimer’s Disease Based on Deep Learning and Hybrid Methods. Electronics. 2021;10(22):2860. doi:10.3390/electronics10222860
35. Prajapati R, Khatri U, Kwon GR. An Efficient Deep Neural Network Binary Classifier for Alzheimer’s Disease Classification. In: 2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC). IEEE; 2021:231-234. doi:10.1109/ICAIIC51459.2021.9415212
36. Bansal D, Khanna K, Chhikara R, Dua RK, Malhotra R. Classification of Magnetic Resonance Images using Bag of Features for Detecting Dementia. Procedia Comput Sci. 2020;167:131-137. doi:10.1016/j.procs.2020.03.190
37. Bidani A, Gouider MS, Travieso-González CM. Dementia Detection and Classification from MRI Images Using Deep Neural Networks and Transfer Learning. In: Rojas I, Joya G, Catala A, eds. Advances in Computational Intelligence. Vol 11506. Lecture Notes in Computer Science. Springer International Publishing; 2019:925-933. doi:10.1007/978-3-030-20521-8_75