Efficient Algorithm for Distinction Mild Cognitive Impairment from Alzheimer’s Disease Based on Specific View FCM White Matter Segmentation and Ensemble Learning
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Abstract
Purpose: Alzheimer's Disease (AD) is in the dementia group and is one of the most prevalent neurodegenerative disorders. Between existing characteristics, White Matter (WM) is a known marker for AD tracking, and WM segmentation in MRI based on clustering can be used to decrease the volume of data. Many algorithms have been developed to predict AD, but most concentrate on the distinction of AD from Cognitive Normal (CN). In this study, we provided a new, simple, and efficient methodology for classifying patients into AD and MCI patients and evaluated the effect of the view dimension of Fuzzy C Means (FCM) in prediction with ensemble classifiers.
Materials and Methods: We proposed our methodology in three steps; first, segmentation of WM from T1 MRI with FCM according to two specific viewpoints (3D and 2D). In the second, two groups of features are extracted: approximate coefficients of Discrete Wavelet Transform (DWT) and statistical (mean, variance, skewness) features. In the final step, an ensemble classifier that is constructed with three classifiers, K-Nearest Neighbor (KNN), Decision Tree (DT), and Linear Discriminant Analysis (LDA), was used.
Results: The proposed method has been evaluated by using 1280 slices (samples) from 64 patients with MCI (32) and AD (32) of the ADNI dataset. The best performance is for the 3D viewpoint, and the accuracy, precision, and f1-score achieved from the methodology are 94.22%, 94.45%, and 94.21%, respectively, by using a ten-fold Cross-Validation (CV) strategy.
Conclusion: The experimental evaluation shows that WM segmentation increases the performance of the ensemble classifier, and moreover the 3D view FCM is better than the 2D view. According to the results, the proposed methodology has comparable performance for the detection of MCI from AD. The low computational cost algorithm and the three classifiers for generalization can be used in practical application by physicians in pre-clinical.
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13- Xiaojuan Guo et al., "Voxel-based assessment of gray and white matter volumes in Alzheimer's disease." Neuroscience letters, Vol. 468 (No. 2), pp. 146-50, (2010).
14- Lars Frings et al., "Longitudinal grey and white matter changes in frontotemporal dementia and Alzheimer’s disease." PloS one, Vol. 9 (No. 3), p. e90814, (2014).
15- Muhammad Tanveer et al., "Machine learning techniques for the diagnosis of Alzheimer’s disease: A review." ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Vol. 16 (No. 1s), pp. 1-35, (2020).
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20- M Paz Sesmero, Agapito I Ledezma, and Araceli Sanchis, "Generating ensembles of heterogeneous classifiers using stacked generalization." Wiley interdisciplinary reviews: data mining and knowledge discovery, Vol. 5 (No. 1), pp. 21-34, (2015).
21- Andreas Holzinger, "Interactive machine learning for health informatics: when do we need the human-in-the-loop?" Brain Informatics, Vol. 3 (No.2, pp. 119-31, (2016).
22- Andreas Holzinger, Markus Plass, Katharina Holzinger, Gloria Cerasela Crişan, Camelia-M Pintea, and Vasile Palade, "Towards interactive Machine Learning (iML): applying ant colony algorithms to solve the traveling salesman problem with the human-in-the-loop approach." in International Conference on Availability, Reliability, and Security, (2016): Springer, pp. 81-95.
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25- Saruar Alam, Goo-Rak Kwon, Ji-In Kim, and Chun-Su Park, "Twin SVM-based classification of Alzheimer’s disease using complex dual-tree wavelet principal coefficients and LDA." Journal of healthcare engineering, Vol. 2017, (2017).
26- AB Al-Khafaji, "Classification of MRI Brain Images using Discrete Wavelet Transform And K-NN." International Journal Of Engineering Sciences & Research Technology, Vol. 4 (No. 11), (2015).
27- Hashem Kalbkhani, Mahrokh G Shayesteh, and Behrooz Zali-Vargahan, "Robust algorithm for brain magnetic resonance image (MRI) classification based on GARCH variances series." Biomedical Signal Processing and Control, Vol. 8 (No. 6), pp. 909-19, (2013).
28- Debesh Jha, Ji-In Kim, Moo-Rak Choi, and Goo-Rak Kwon, "Pathological brain detection using weiner filtering, 2D-discrete wavelet transform, probabilistic PCA, and random subspace ensemble classifier." Computational intelligence and neuroscience, Vol. 2017, (2017).
29- Alexia Tzalavra et al., "Comparison of multi-resolution analysis patterns for texture classification of breast tumors based on DCE-MRI." in International Workshop on Machine Learning in Medical Imaging, (2016): Springer, pp. 296-304.
30- Bekir Karlιk, Yücel Koçyiğit, and Mehmet Korürek, "Differentiating types of muscle movements using a wavelet based fuzzy clustering neural network." Expert Systems, Vol. 26 (No. 1), pp. 49-59, (2009).
31- N Hema Rajini and R Bhavani, "Classification of MRI brain images using k-nearest neighbor and artificial neural network." in 2011 International conference on recent trends in information technology (ICRTIT), (2011): IEEE, pp.563-68.
32- Muhammad Nazir, Fazli Wahid, and Sajid Ali Khan, "A simple and intelligent approach for brain MRI classification." Journal of Intelligent & Fuzzy Systems, Vol. 28 (No. 3), pp. 1127-35, (2015).
33- Xindong Wu et al., "Top 10 algorithms in data mining." Knowledge and information systems, Vol. 14 (No. 1), pp. 1-37, (2008).
34- El Mehdi Benyoussef, Abdeltif Elbyed, and Hind El Hadiri, "3D MRI classification using KNN and deep neural network for Alzheimer’s disease diagnosis." in International Conference on Advanced Intelligent Systems for Sustainable Development, (2018): Springer, pp. 154-58.
35- M Evanchalin Sweety and G Wiselin Jiji, "Detection of Alzheimer disease in brain images using PSO and Decision Tree Approach." in 2014 IEEE International Conference on Advanced Communications, Control and Computing Technologies, (2014): IEEE, pp. 1305-09.
36- S Naganandhini and P Shanmugavadivu, "Effective diagnosis of Alzheimer’s disease using modified decision tree classifier." Procedia Computer Science, Vol. 165pp. 548-55, (2019).
37- Usha BL and Supreeth HSG, "Brain Tumor detection and identification in brain MRI using supervised learning: A LDA based classification method." Image, Vol. 1p. 2, (2107).
38- Yong Zhang, Xiaobo Zhou, Rochelle M Witt, Bernardo L Sabatini, Donald Adjeroh, and Stephen TC Wong, "Dendritic spine detection using curvilinear structure detector and LDA classifier." NeuroImage, Vol. 36 (No. 2), pp. 346-60, (2007).
39- Lorenza Saitta, "Integrated architectures for machine learning." in Advanced Course on Artificial Intelligence, (1999): Springer, pp. 218-29.
40- Ludmila I Kuncheva, "Switching between selection and fusion in combining classifiers: An experiment." IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), Vol. 32 (No. 2), pp. 146-56, (2002).
41- Xingquan Zhu, Xindong Wu, and Ying Yang, "Dynamic classifier selection for effective mining from noisy data streams." in Fourth IEEE International Conference on Data Mining (ICDM'04), (2004): IEEE, pp. 305-12.
42- Laila Khedher, Javier Ramírez, Juan Manuel Górriz, Abdelbasset Brahim, and IA Illán, "Independent component analysis-based classification of Alzheimer’s disease from segmented MRI data." in International Work-Conference on the Interplay between Natural and Artificial Computation, (2015): Springer, pp. 78-87.
43- Andrés Ortiz, Jorge Munilla, Ignacio Álvarez-Illán, Juan M Górriz, Javier Ramírez, and Alzheimer's Disease Neuroimaging Initiative, "Exploratory graphical models of functional and structural connectivity patterns for Alzheimer's Disease diagnosis." Frontiers in computational neuroscience, Vol. 9p. 132, (2015).
44- Saruar Alam, Goo‐Rak Kwon, and Alzheimer's Disease Neuroimaging Initiative, "Alzheimer disease classification using KPCA, LDA, and multi‐kernel learning SVM." International Journal of Imaging Systems and Technology, Vol. 27 (No. 2), pp. 133-43, (2017).
45- Laila Khedher, Ignacio A Illán, Juan M Górriz, Javier Ramírez, Abdelbasset Brahim, and Anke Meyer-Baese, "Independent component analysis-support vector machine-based computer-aided diagnosis system for Alzheimer’s with visual support." International journal of neural systems, Vol. 27 (No. 03), p. 1650050, (2017).
46- Jin Liu, Jianxin Wang, Bin Hu, Fang-Xiang Wu, and Yi Pan, "Alzheimer’s disease classification based on individual hierarchical networks constructed with 3-D texture features." IEEE transactions on nanobioscience, Vol. 16 (No. 6), pp. 428-37, (2017).
47- Zhuo Sun, Yuchuan Qiao, Boudewijn PF Lelieveldt, Marius Staring, and Alzheimer's Disease NeuroImaging Initiative, "Integrating spatial-anatomical regularization and structure sparsity into SVM: Improving interpretation of Alzheimer's disease classification." NeuroImage, Vol. 178pp. 445-60, (2018).
48- Jin Liu, Min Li, Wei Lan, Fang-Xiang Wu, Yi Pan, and Jianxin Wang, "Classification of Alzheimer's disease using whole brain hierarchical network." IEEE/ACM transactions on computational biology and bioinformatics, Vol. 15 (No. 2), pp. 624-32, (2016).
49- Bo Cheng, Mingxia Liu, Daoqiang Zhang, and Dinggang Shen, "Robust multi-label transfer feature learning for early diagnosis of Alzheimer’s disease." Brain imaging and behavior, Vol. 13 (No. 1), pp. 138-53, (2019).
50- Weihao Zheng, Zhijun Yao, Yuanwei Xie, Jin Fan, and Bin Hu, "Identification of Alzheimer’s disease and mild cognitive impairment using networks constructed based on multiple morphological brain features." Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, Vol. 3 (No. 10), pp. 887-97, (2018).
51- Krishnakumar Vaithinathan, Latha Parthiban, and Alzheimer's Disease Neuroimaging Initiative, "A novel texture extraction technique with T1 weighted MRI for the classification of Alzheimer’s disease." Journal of neuroscience methods, Vol. 318pp. 84-99, (2019).
52- Ali Ezzati et al., "Optimizing machine learning methods to improve predictive models of Alzheimer’s disease." Journal of Alzheimer's Disease, Vol. 71 (No. 3), pp. 1027-36, (2019).
53- Jialin Peng, Xiaofeng Zhu, Ye Wang, Le An, and Dinggang Shen, "Structured sparsity regularized multiple kernel learning for Alzheimer’s disease diagnosis." Pattern recognition, Vol. 88pp. 370-82, (2019).
2- Heba Mohsen, El-Sayed A El-Dahshan, El-Sayed M El-Horbaty, and Abdel-Badeeh M Salem, "Classification of brain MRI for Alzheimer's disease based on linear discriminate analysis." Egyptian Computer Science Journal, Vol. 41 (No. 3), pp. 44-52, (2017).
3- Fabio Previtali, Paola Bertolazzi, Giovanni Felici, and Emanuel Weitschek, "A novel method and software for automatically classifying Alzheimer’s disease patients by magnetic resonance imaging analysis." Computer methods and programs in biomedicine, Vol. 143pp. 89-95, (2017).
4- KR Kruthika, Akshay Pai, HD Maheshappa, and Alzheimer’s disease Neuroimaging Initiative, "Classification of alzheimer and MCI phenotypes on MRI data using SVM." in International Symposium on Signal Processing and Intelligent Recognition Systems, (2017): Springer, pp. 263-75.
5- Debesh Jha, Ji-In Kim, and Goo-Rak Kwon, "Diagnosis of Alzheimer’s disease using dual-tree complex wavelet transform, PCA, and feed-forward neural network." Journal of healthcare engineering, Vol. 2017, (2017).
6- Barbara J Grabher, "Effects of Alzheimer disease on patients and their family." Journal of nuclear medicine technology, Vol. 46 (No. 4), pp. 335-40, (2018).
7- Hanns Hippius and Gabriele Neundörfer, "The discovery of Alzheimer's disease." Dialogues in clinical neuroscience, ( (2022).
8- U Rajendra Acharya et al., "Automated detection of Alzheimer’s disease using brain MRI images–a study with various feature extraction techniques." Journal of Medical Systems, Vol. 43 (No. 9), pp. 1-14, (2019).
9- Sitara Afzal et al., "Alzheimer disease detection techniques and methods: a review."(2021).
10- Ajax E George et al., "CT diagnostic features of Alzheimer disease: importance of the choroidal/hippocampal fissure complex." American journal of Neuroradiology, Vol. 11 (No. 1), pp. 101-07, (1990).
11- Sara E Nasrabady, Batool Rizvi, James E Goldman, and Adam M Brickman, "White matter changes in Alzheimer’s disease: a focus on myelin and oligodendrocytes." Acta neuropathologica communications, Vol. 6 (No. 1), pp. 1-10, (2018).
12- MLF Balthazar, CL Yasuda, FR Pereira, T Pedro, BP Damasceno, and F Cendes, "Differences in grey and white matter atrophy in amnestic mild cognitive impairment and mild Alzheimer’s disease." European journal of neurology, Vol. 16 (No. 4), pp. 468-74, (2009).
13- Xiaojuan Guo et al., "Voxel-based assessment of gray and white matter volumes in Alzheimer's disease." Neuroscience letters, Vol. 468 (No. 2), pp. 146-50, (2010).
14- Lars Frings et al., "Longitudinal grey and white matter changes in frontotemporal dementia and Alzheimer’s disease." PloS one, Vol. 9 (No. 3), p. e90814, (2014).
15- Muhammad Tanveer et al., "Machine learning techniques for the diagnosis of Alzheimer’s disease: A review." ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Vol. 16 (No. 1s), pp. 1-35, (2020).
16- Soheil Ahmadzadeh Irandoost and Fatemeh Asadi, "Classification of brain MRI for Alzheimer’s disease detection based on ensemble machine learning." Iranian Journal of Radiology, Vol. 16 (No. Special Issue), (2019).
17- Stanisław Adaszewski, Juergen Dukart, Ferath Kherif, Richard Frackowiak, Bogdan Draganski, and Alzheimer's Disease Neuroimaging Initiative, "How early can we predict Alzheimer's disease using computational anatomy?" Neurobiology of aging, Vol. 34 (No. 12), pp. 2815-26, (2013).
18- Stefan Klöppel et al., "Automatic classification of MR scans in Alzheimer's disease." Brain, Vol. 131 (No. 3), pp. 681-89, (2008).
19- KR Kruthika, HD Maheshappa, and Alzheimer's Disease Neuroimaging Initiative, "Multistage classifier-based approach for Alzheimer's disease prediction and retrieval." Informatics in Medicine Unlocked, Vol. 14pp. 34-42, (2019).
20- M Paz Sesmero, Agapito I Ledezma, and Araceli Sanchis, "Generating ensembles of heterogeneous classifiers using stacked generalization." Wiley interdisciplinary reviews: data mining and knowledge discovery, Vol. 5 (No. 1), pp. 21-34, (2015).
21- Andreas Holzinger, "Interactive machine learning for health informatics: when do we need the human-in-the-loop?" Brain Informatics, Vol. 3 (No.2, pp. 119-31, (2016).
22- Andreas Holzinger, Markus Plass, Katharina Holzinger, Gloria Cerasela Crişan, Camelia-M Pintea, and Vasile Palade, "Towards interactive Machine Learning (iML): applying ant colony algorithms to solve the traveling salesman problem with the human-in-the-loop approach." in International Conference on Availability, Reliability, and Security, (2016): Springer, pp. 81-95.
23- [Online]. Available: http://adni.loni.usc.edu/.
24- Lei Hua, Yi Gu, Xiaoqing Gu, Jing Xue, and Tongguang Ni, "A novel brain MRI image segmentation method using an improved multi-view fuzzy c-means clustering algorithm." Frontiers in Neuroscience, Vol. 15p. 662674, (2021).
25- Saruar Alam, Goo-Rak Kwon, Ji-In Kim, and Chun-Su Park, "Twin SVM-based classification of Alzheimer’s disease using complex dual-tree wavelet principal coefficients and LDA." Journal of healthcare engineering, Vol. 2017, (2017).
26- AB Al-Khafaji, "Classification of MRI Brain Images using Discrete Wavelet Transform And K-NN." International Journal Of Engineering Sciences & Research Technology, Vol. 4 (No. 11), (2015).
27- Hashem Kalbkhani, Mahrokh G Shayesteh, and Behrooz Zali-Vargahan, "Robust algorithm for brain magnetic resonance image (MRI) classification based on GARCH variances series." Biomedical Signal Processing and Control, Vol. 8 (No. 6), pp. 909-19, (2013).
28- Debesh Jha, Ji-In Kim, Moo-Rak Choi, and Goo-Rak Kwon, "Pathological brain detection using weiner filtering, 2D-discrete wavelet transform, probabilistic PCA, and random subspace ensemble classifier." Computational intelligence and neuroscience, Vol. 2017, (2017).
29- Alexia Tzalavra et al., "Comparison of multi-resolution analysis patterns for texture classification of breast tumors based on DCE-MRI." in International Workshop on Machine Learning in Medical Imaging, (2016): Springer, pp. 296-304.
30- Bekir Karlιk, Yücel Koçyiğit, and Mehmet Korürek, "Differentiating types of muscle movements using a wavelet based fuzzy clustering neural network." Expert Systems, Vol. 26 (No. 1), pp. 49-59, (2009).
31- N Hema Rajini and R Bhavani, "Classification of MRI brain images using k-nearest neighbor and artificial neural network." in 2011 International conference on recent trends in information technology (ICRTIT), (2011): IEEE, pp.563-68.
32- Muhammad Nazir, Fazli Wahid, and Sajid Ali Khan, "A simple and intelligent approach for brain MRI classification." Journal of Intelligent & Fuzzy Systems, Vol. 28 (No. 3), pp. 1127-35, (2015).
33- Xindong Wu et al., "Top 10 algorithms in data mining." Knowledge and information systems, Vol. 14 (No. 1), pp. 1-37, (2008).
34- El Mehdi Benyoussef, Abdeltif Elbyed, and Hind El Hadiri, "3D MRI classification using KNN and deep neural network for Alzheimer’s disease diagnosis." in International Conference on Advanced Intelligent Systems for Sustainable Development, (2018): Springer, pp. 154-58.
35- M Evanchalin Sweety and G Wiselin Jiji, "Detection of Alzheimer disease in brain images using PSO and Decision Tree Approach." in 2014 IEEE International Conference on Advanced Communications, Control and Computing Technologies, (2014): IEEE, pp. 1305-09.
36- S Naganandhini and P Shanmugavadivu, "Effective diagnosis of Alzheimer’s disease using modified decision tree classifier." Procedia Computer Science, Vol. 165pp. 548-55, (2019).
37- Usha BL and Supreeth HSG, "Brain Tumor detection and identification in brain MRI using supervised learning: A LDA based classification method." Image, Vol. 1p. 2, (2107).
38- Yong Zhang, Xiaobo Zhou, Rochelle M Witt, Bernardo L Sabatini, Donald Adjeroh, and Stephen TC Wong, "Dendritic spine detection using curvilinear structure detector and LDA classifier." NeuroImage, Vol. 36 (No. 2), pp. 346-60, (2007).
39- Lorenza Saitta, "Integrated architectures for machine learning." in Advanced Course on Artificial Intelligence, (1999): Springer, pp. 218-29.
40- Ludmila I Kuncheva, "Switching between selection and fusion in combining classifiers: An experiment." IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), Vol. 32 (No. 2), pp. 146-56, (2002).
41- Xingquan Zhu, Xindong Wu, and Ying Yang, "Dynamic classifier selection for effective mining from noisy data streams." in Fourth IEEE International Conference on Data Mining (ICDM'04), (2004): IEEE, pp. 305-12.
42- Laila Khedher, Javier Ramírez, Juan Manuel Górriz, Abdelbasset Brahim, and IA Illán, "Independent component analysis-based classification of Alzheimer’s disease from segmented MRI data." in International Work-Conference on the Interplay between Natural and Artificial Computation, (2015): Springer, pp. 78-87.
43- Andrés Ortiz, Jorge Munilla, Ignacio Álvarez-Illán, Juan M Górriz, Javier Ramírez, and Alzheimer's Disease Neuroimaging Initiative, "Exploratory graphical models of functional and structural connectivity patterns for Alzheimer's Disease diagnosis." Frontiers in computational neuroscience, Vol. 9p. 132, (2015).
44- Saruar Alam, Goo‐Rak Kwon, and Alzheimer's Disease Neuroimaging Initiative, "Alzheimer disease classification using KPCA, LDA, and multi‐kernel learning SVM." International Journal of Imaging Systems and Technology, Vol. 27 (No. 2), pp. 133-43, (2017).
45- Laila Khedher, Ignacio A Illán, Juan M Górriz, Javier Ramírez, Abdelbasset Brahim, and Anke Meyer-Baese, "Independent component analysis-support vector machine-based computer-aided diagnosis system for Alzheimer’s with visual support." International journal of neural systems, Vol. 27 (No. 03), p. 1650050, (2017).
46- Jin Liu, Jianxin Wang, Bin Hu, Fang-Xiang Wu, and Yi Pan, "Alzheimer’s disease classification based on individual hierarchical networks constructed with 3-D texture features." IEEE transactions on nanobioscience, Vol. 16 (No. 6), pp. 428-37, (2017).
47- Zhuo Sun, Yuchuan Qiao, Boudewijn PF Lelieveldt, Marius Staring, and Alzheimer's Disease NeuroImaging Initiative, "Integrating spatial-anatomical regularization and structure sparsity into SVM: Improving interpretation of Alzheimer's disease classification." NeuroImage, Vol. 178pp. 445-60, (2018).
48- Jin Liu, Min Li, Wei Lan, Fang-Xiang Wu, Yi Pan, and Jianxin Wang, "Classification of Alzheimer's disease using whole brain hierarchical network." IEEE/ACM transactions on computational biology and bioinformatics, Vol. 15 (No. 2), pp. 624-32, (2016).
49- Bo Cheng, Mingxia Liu, Daoqiang Zhang, and Dinggang Shen, "Robust multi-label transfer feature learning for early diagnosis of Alzheimer’s disease." Brain imaging and behavior, Vol. 13 (No. 1), pp. 138-53, (2019).
50- Weihao Zheng, Zhijun Yao, Yuanwei Xie, Jin Fan, and Bin Hu, "Identification of Alzheimer’s disease and mild cognitive impairment using networks constructed based on multiple morphological brain features." Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, Vol. 3 (No. 10), pp. 887-97, (2018).
51- Krishnakumar Vaithinathan, Latha Parthiban, and Alzheimer's Disease Neuroimaging Initiative, "A novel texture extraction technique with T1 weighted MRI for the classification of Alzheimer’s disease." Journal of neuroscience methods, Vol. 318pp. 84-99, (2019).
52- Ali Ezzati et al., "Optimizing machine learning methods to improve predictive models of Alzheimer’s disease." Journal of Alzheimer's Disease, Vol. 71 (No. 3), pp. 1027-36, (2019).
53- Jialin Peng, Xiaofeng Zhu, Ye Wang, Le An, and Dinggang Shen, "Structured sparsity regularized multiple kernel learning for Alzheimer’s disease diagnosis." Pattern recognition, Vol. 88pp. 370-82, (2019).
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| Issue | Vol 10 No 3 (2023) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/fbt.v10i3.13158 | |
| Keywords | ||
| Alzheimer's Disease Fuzzy C Means Ensemble | ||
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How to Cite
1.
Ahmadzade Irandoost S, Mirafzali Saryazdi FS. Efficient Algorithm for Distinction Mild Cognitive Impairment from Alzheimer’s Disease Based on Specific View FCM White Matter Segmentation and Ensemble Learning. Frontiers Biomed Technol. 2023;10(3):287-298.
