Conv-ERVFL: Convolutional Neural Network Based Ensemble RVFL Classifier for Alzheimer’s Disease Diagnosis
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As per the latest statistics, Alzheimer's disease (AD) has become a global burden over the following decades. Identifying AD at the intermediate stage became challenging, with mild cognitive impairment (MCI) utilizing credible biomarkers and robust learning approaches. Neuroimaging techniques like magnetic resonance imaging (MRI) and positron emission tomography (PET) are practical research approaches that provide structural atrophies and metabolic variations. With the help of MRI and PET scans, metabolic and structural changes in AD patients can be visible even ten years before the disease's onset. This paper proposes a novel wavelet packet transform-based structural and metabolic image fusion approach using MRI and PET scans. An eight-layer trained CNN extracts features from multiple layers and these features are fed to an ensemble of non-iterative random vector functional link (RVFL) models. The RVFL network incorporates the s-membership fuzzy function as an activation function that helps overcome outliers. Lastly, outputs of all the customized RVFL classifiers are averaged and fed to the RVFL classifier to make the final decision. Experiments are performed over Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, and classification is made over CN vs. AD vs. MCI. The model performance obtained is decent enough to prove the effectiveness of the fusion-based ensemble approach.
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Sharma et al. achieved 0.9603 accuracy for AD vs. NC vs. MCI and 0.7521 AUC on ADNI using fused image by wavelet packet transform (WPT) from MRI and PET.
“| Sharma et al. [80] | Fused image by wavelet packet transform (WPT) | MRI, PET | ADNI | 0.9603 (ACC: AD vs. NC vs. MCI) 0.7521 (AUC) |”
From A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis (2024) · cites this paper as [80]
Cites
Knopman, D. S., Amieva, H., Petersen, R. C., Chételat, G., Holtzman, D. M., Hyman, B. T., Nixon, R. A., & Jones, D. T. (2021) · Nature Reviews Disease Primers · doi:10.1038/s41572-021-00269-y · Cited by 2,606 · see citing works
Stereotaxic Display of Brain Lesions
Rorden, C., & Brett, M. (2000) · Behavioural Neurology · doi:10.1155/2000/421719 · Cited by 2,573 · see citing works
The clinical use of structural MRI in Alzheimer disease
Frisoni, G. B., Fox, N. C., Jack, C. R., Scheltens, P., & Thompson, P. M. (2010) · Nature Reviews Neurology · doi:10.1038/nrneurol.2009.215 · Cited by 2,113 · see citing works
Functional-link net computing: theory, system architecture, and functionalities
Pao, Y.-H., & Takefuji, Y. (1992) · Computer · doi:10.1109/2.144401 · Cited by 934 · see citing works
Statistical Parametric Mapping
Friston, K. J. (2003) · Springer eBooks · doi:10.1007/978-1-4615-1079-6_16 · Cited by 690 · see citing works
The use of PET in Alzheimer disease
Nordberg, A. K., Rinne, J. O., Kadir, A., & Långström, B. (2010) · Nature Reviews Neurology · doi:10.1038/nrneurol.2009.217 · Cited by 451 · see citing works
Ensemble deep learning in bioinformatics
Cao, Y., Geddes, T. A., Yang, J. Y. H., & Yang, P. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-0217-y · Cited by 357 · see citing works
Machine Learning Techniques for the Diagnosis of Alzheimer’s Disease
Tanveer, M., Richhariya, B., Khan, R. U., Rashid, A. H., Khanna, P., Prasad, M., & Lin, C. (2020) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3344998 · Cited by 334 · see citing works
Self-citationEMFusion: An unsupervised enhanced medical image fusion network
Xu, H., & Ma, J. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.06.001 · Cited by 320 · see citing works
Multimodal medical image fusion review: Theoretical background and recent advances
Hermessi, H., Mourali, O., & Zagrouba, E. (2021) · Signal Processing · doi:10.1016/j.sigpro.2021.108036 · Cited by 306 · see citing works
Random vector functional link neural network based ensemble deep learning
Shi, Q., Katuwal, R., Suganthan, P. N., & Tanveer, M. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.107978 · Cited by 289 · see citing works
Self-citationThe ensemble deep learning model for novel COVID-19 on CT images
Zhou, T., Lu, H., Yang, Z., Qiu, S., Huo, B., & Dong, Y. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106885 · Cited by 255 · see citing works
Kim, P. (2017) · Apress eBooks · doi:10.1007/978-1-4842-2845-6_6 · Cited by 224 · see citing works
Deep ensemble learning for Alzheimer's disease classification
An, N., Ding, H., Yang, J., Au, R., & Ang, T. F. A. (2020) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2020.103411 · Cited by 203 · see citing works
Richhariya, B., Tanveer, M., & Rashid, A. H. (2020) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2020.101903 · Cited by 190 · see citing works
Self-citationZhang, Y., Wang, S., Xia, K., Jiang, Y., & Qian, P. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.09.002 · Cited by 184 · see citing works
Tanveer, M., Rashid, A. H., Ganaie, M. A., Reza, M., Razzak, I., & Hua, K. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3083274 · Cited by 177 · see citing works
Self-citationLoddo, A., Buttau, S., & Ruberto, C. D. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.105032 · Cited by 165 · see citing works
An unsupervised parameter learning model for RVFL neural network
Zhang, Y., Wu, J. X., Cai, Z., et al. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.01.007 · Cited by 131 · see citing works
Zhang, L., & Suganthan, P. N. (2017) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2017.2742867 · Cited by 117 · see citing works
Self-citationTwo-Stage Selective Ensemble of CNN via Deep Tree Training for Medical Image Classification
Yang, Y., Hu, Y., Zhang, X., & Wang, S. (2021) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2021.3061147 · Cited by 116 · see citing works
Gao, R., Du, L., Suganthan, P. N., Zhou, Q., & Yuen, K. F. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.117784 · Cited by 116 · see citing works
Self-citationPredicting Brain Age Using Machine Learning Algorithms: A Comprehensive Evaluation
Beheshti, I., Ganaie, M. A., Paliwal, V., et al. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3083187 · Cited by 108 · see citing works
PET Neuroimaging of Alzheimer's Disease: Radiotracers and Their Utility in Clinical Research
Bao, W., Xie, F., Zuo, C., Guan, Y., & Huang, Y. H. (2021) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2021.624330 · Cited by 102 · see citing works
Zeisel, J., Bennett, K., & Fleming, R. (2020) · Cited by 97 · see citing works
Sharma, R., Goel, T., Tanveer, M., & Murugan, R. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2021.108099 · Cited by 90 · see citing works
Self-citationKatuwal, R., Suganthan, P. N., & Zhang, L. (2017) · Applied Soft Computing · doi:10.1016/j.asoc.2017.09.020 · Cited by 86 · see citing works
Self-citationAymaz, S., & Köse, C. (2018) · Information Fusion · doi:10.1016/j.inffus.2018.01.015 · Cited by 78 · see citing works
PET and MRI image fusion based on combination of 2-D Hilbert transform and IHS method
Haddadpour, M., Danishvar, S., & Seyedarabi, H. (2017) · Biomedical Journal · doi:10.1016/j.bj.2017.05.002 · Cited by 77 · see citing works
Hedayati, R., Khedmati, M., & Taghipour‐Gorjikolaie, M. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2020.102397 · Cited by 76 · see citing works
PET/MR Imaging: New Frontier in Alzheimer's Disease and Other Dementias
Zhang, X. Y., Yang, Z. L., Lu, G., Yang, G. F., & Zhang, L. J. (2017) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2017.00343 · Cited by 70 · see citing works
Sharma, R., Goel, T., Tanveer, M., Dwivedi, S., & Murugan, R. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2021.107371 · Cited by 61 · see citing works
Self-citationA low-complexity fuzzy activation function for artificial neural networks
Soria‐Olivas, E., Martín‐Guerrero, J. D., Camps‐Valls, G., et al. (2003) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2003.820444 · Cited by 48 · see citing works
Shibu, D. S., & Priyadharsini, S. S. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.102789 · Cited by 44 · see citing works
Diagnostic value of retrospective PET-MRI fusion in head-and-neck cancer
Loeffelbein, D. J., Souvatzoglou, M., Wankerl, V., et al. (2014) · BMC Cancer · doi:10.1186/1471-2407-14-846 · Cited by 35 · see citing works
Early Alzheimer’s disease diagnosis with the contrastive loss using paired structural MRIs
Qiao, H., Chen, L., Ye, Z., & Zhu, F. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106282 · Cited by 31 · see citing works
Relationship between tau, neuroinflammation and atrophy in Alzheimer's disease: The NIMROD study
Su, L., Surendranathan, A., Huang, Y., Bevan-Jones, W. R., Passamonti, L., Hong, Y. T., Arnold, R. J., Rodríguez, P. V., Wang, Y., Mak, E., et al. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.10.006 · Cited by 30 · see citing works
Remarks on the local Fourier bases
Auscher, P. (2021) · CRC Press eBooks · doi:10.1201/9781003210450-6 · Cited by 20 · see citing works
A Novel Image Fusion Method Based on Wavelet Packet Transform
Chen, H., Liu, Y., & Wang, Y. (2008) · doi:10.1109/kamw.2008.4810524 · Cited by 16 · see citing works
An efficient multi transform based fusion for multi focus images
Paramanandham, N., Rajendiran, K., Narayanan, D., et al. (2015) · International Conference on Communications and Signal Processing (ICCSP) · doi:10.1109/iccsp.2015.7322645 · Cited by 13 · see citing works
Medical image processing based on multidimensional wavelet transforms - Advantages and trends
Georgieva, V. M., Petrov, P. P., & Zlatareva, D. (2021) · AIP conference proceedings · doi:10.1063/5.0041869 · Cited by 10 · see citing works
Multi-focus Image Fusion Based on Fuzzy and Wavelet Transform
Saeedi, J., Faez, K., & Mozaffari, S. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-10268-4_113 · Cited by 9 · see citing works
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A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis
Xu, X., Li, J., Zhu, Z., Zhao, L., Wang, H., Song, C., Chen, Y., Zhao, Q., Yang, J., & Pei, Y. (2024) · Bioengineering · doi:10.3390/bioengineering11030219 · Cited by 242 · see citing works
- Authors
- Sharma, R., Goel, T., Tanveer, M., Suganthan, P. N., Razzak, I., & Murugan, R.
- Affiliations
- National Institute Of Technology Silchar (IN)
- Indian Institute of Technology Indore (IN)
- Nanyang Technological University (SG)
- UNSW Sydney (AU)
- Venue
- IEEE Journal of Biomedical and Health Informatics
- Published
- 2023-10
- Volume
- 27
- Issue
- 10
- Pages
- 4995-5003
- ISSN
- 2168-2194 (ISSN-L)
- 2168-2208
- OpenAlex
- W4306831518
- Cited by
- 44
- References
- 42
- Citations by year
- 2026: 7 · 2025: 12 · 2024: 19 · 2023: 6
- FWCI
- 6.06
- Type
- journal article
- Language
- en
- Open access
- green
- figshare.com
- Licence
- Locations
- IEEE Journal of Biomedical and Health Informatics (publishedVersion)
- PubMed (publishedVersion)
- DR-NTU (Nanyang Technological University) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- Qatar University QSpace (Qatar University) (submittedVersion)
- Funders
- National Supercomputing Mission
- DST and MeitY, Govt. of India (DST/NSM/R&D_HPC_Appl/2021/03.29)
- Science and Engineering Research Board
- Mathematical Research Impact-Centric Support (MTR/2021/000787)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Keywords
- Alzheimer's disease diagnosis, mild cognitive impairment, random vector functional link, ensemble classifiers, convolutional neural networks, wavelet packet decomposition, outlier robustness, ADNI dataset
- MeSH terms
- Alzheimer Disease, Humans, Magnetic Resonance Imaging, Neural Networks, Computer, Positron-Emission Tomography, Neuroimaging, Cognitive Dysfunction
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