A Multifunctional Approach to Feature Extraction from fMRI Images in Alzheimer's disease
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Abstract
Introduction: The use of fMRI imaging in medical science has led to the diagnosis of diseases at the very first stages before the disease get advancedwhich plays a significant role in some diseases such as Alzheimer's. Extracting useful information from these images is the first step in the initial diagnosis of the disease that the accuracy in extracting as much of this information as possible contributes significantly to the initial diagnosis. Increases the speed of processing and estimation accuracy which was done in the present study using a multi-purpose method. While in recent studies, simpler methods with a limited number of features were used.
Methods: The information of 140 patients with Alzheimer's disease was obtained, and the stable multipurpose feature extraction method was used to extract the information. In this way, two-level wavelet, modeling of wavelet coefficients, normalization method and feature selection are applied.
Results: The results obtained from the examination of 285 features in five categories showed that some of the information contained in the features overlapped and lacked useful information. In addition, dimensionality and noise reduction using the PCA algorithm showed that about 41% of the relevant features are outliers or missing information.
Cunclusion:In general, increasing the speed of processing and estimation accuracy which was done in the present study using a multi-purpose method. While in recent studies, simpler methods with a limited number of features were used.
2. Korolev, I.O., 2014. Alzheimer’s disease: a clinical and basic science review. Med. Student Res. J. 4 (1), 24–33.
3. Moon SW, Lee B, Choi YC. Changes in the hippocampal volume and shape in early-onset mild cognitive impairment. Psychiatry Invest 2018; 15 (5): 531–537.
4. Brody H. Medical imaging. Nat. Cell Biol. 2013; 502, S81.
5. Yousaf T; Dervenoulas G; Politis M. Chapter Two-Advances in MRI Methodology. In International Review of Neurobiology; Politis, M., Ed.; Academic Press: Cambridge, MA, USA 2018; 141: 31–76.
6. Akhavan Aghdam M; Sharifi A; Pedram MM. Combination of rs-fMRI and sMRI Data to Discriminate Autism Spectrum Disorders in Young Children Using Deep Belief Network. J. Digit Imaging 2018; 31: 895–903.
7. Sarraf S, Tofighi G, Alzheimer's Disease Neuroimaging Initiative. DeepAD: Alzheimer's disease classification via deep convolutional neural networks using MRI and fMRI. BioRxiv. 2016; 1:070441.
8. Parmar HS, Nutter B, Long R, Antani S, Mitra S. Deep learning of volumetric 3D CNN for fMRI in Alzheimer's disease classification. InMedical Imaging. Biomedical Applications in Molecular, Structural, and Functional Imaging. International Society for Optics and Photonics.2020; 11317: 113170C.
9. Currie G, Hawk KE, Rohren E, Vial A, Klein R. Machine Learning and Deep Learning in Medical Imaging: Intelligent Imaging. J. Med. Imaging Radiat. Sci. 2019; 50: 477–487.
10. Duc NT, Ryu S, Qureshi MN, Choi M, Lee KH, Lee B. 3D-deep learning based automatic diagnosis of Alzheimer's disease with joint MMSE prediction using resting-state fMRI. Neuroinformatics. 2020;18(1):71-86.
11. Erickson BJ, Korfiatis P, Akkus Z, Kline TL. Machine Learning for Medical Imaging. Radiographics 2017; 37: 505–515.
12. Liu S, Liu S, Cai W, Pujol S, Kikinis R, Feng D. Early diagnosis of Alzheimer's disease with deep learning. In2014 IEEE 11th international symposium on biomedical imaging (ISBI) 2014; 29: 1015-1018.
13. Wang SH, Phillips P, Sui Y, Liu B, Yang M, Cheng H. Classification of Alzheimer's disease based on eight-layer convolutional neural network with leaky rectified linear unit and max pooling. Journal of medical systems. 2018; 42(5): 1-1.
14. Ramzan F, Khan MU, Rehmat A, Iqbal S, Saba T, Rehman A, Mehmood Z. A deep learning approach for automated diagnosis and multi-class classification of Alzheimer's disease stages using resting-state fMRI and residual neural networks. Journal of medical systems. 2020; 44(2):1-6.
15. Suk HI, Lee SW, Shen D, Alzheimer's Disease Neuroimaging Initiative. Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis. NeuroImage. 2014; 101:569-82.
16. ADNI. Alzheimer’s disease Neuroimaging Initiative: ADNI. http://adni.loni.usc. edu/data-samples/access-data, accessed: 2020-07-13.
17. Kam TE, Zhang H, Shen D. A Novel Deep Learning Framework on Brain Functional Networks for Early MCI Diagnosis. In Medical Image Computing and Computer Assisted Intervention—MICCAI 2018; Springer Science and Business Media LLC: Cham, Switzerland, 2018; 11072: 293–301.
18. Zheng Y, Guo H, Zhang L, Wu J, Li Q, Lv F. Machine Learning-Based Framework for Differential Diagnosis Between Vascular Dementia and Alzheimer’s Disease Using Structural MRI Features. Front. Neurol. 2019; 10.
19. Khanna A, Tanwar S, Rodrigues JJ, Roy NR. Alzheimer detection using Group Grey Wolf Optimization based features with convolutional classifier. Comput. Electr. Eng. 2019; 77: 230–243.
20. Amini M, Pedram M, Moradi A, Ouchani M. Diagnosis of Alzheimer’s Disease Severity with fMRI Images Using Robust Multitask Feature Extraction Method and Convolutional Neural Network (CNN). Comput. Math. Methods Med 202;1: 1–15.
21. Al-Khuzaie FE, Bayat O, Duru AD. Diagnosis of Alzheimer's disease Using 2D MRI Slices by Convolutional Neural Network. Applied Bionics and Biomechanics. 2021; 2:2021.
22. Acharya UR, Fernandes SL, WeiKoh JE. Automated Detection of Alzheimer’s Disease Using Brain MRI Images– A Study with Various Feature Extraction Techniques. J Med Syst 2019; 43: 30.
23. Dyrba M, Grothe M, Kirste T, Teipel SJ. Multimodal analysis of functional and structural disconnection in Alzheimer's disease using multiple kernel SVM. Hum Brain Mapp 2015; 36: 2118-2131.
24. Dai Z, Yan C, Wang Z, Wang J, Xia M, Li K, He Y. Discriminative analysis of early Alzheimer's disease using multi-modal imaging and multi-level characterization with multi-classifier (M3). Neuroimage 2012; 59: 2187-2195.
25. Silva IRR, Silva GSL, de Souza RG, dos Santos WP, Fagundes RA. "Model Based on Deep Feature Extraction for Diagnosis of Alzheimer’s Disease," 2019 International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary 2019; 1-7
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| Issue | Vol 11 No 2 (2025) | |
| Section | Articles | |
| DOI | https://doi.org/10.18502/jbe.v11i2.20556 | |
| Keywords | ||
| Medical Imaging Brain Diseases fMRI Images Feature Extraction | ||
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