Early detection of alzheimer's disease using polar harmonic transforms and optimized wavelet neural network
Effective and accurate diagnosis of Alzheimer’s disease (AD), as well as early-stage detection, has gained more and more attention in recent years. For AD classification, we propose a new hybrid method for early detection of Alzheimer’s disease (AD) using Polar Harmonic Transforms (PHT) and Self-ada...
| Main Authors: | , , , , |
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| Format: | Journal Article |
| Language: | English |
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2021
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| Online Access: | https://hdl.handle.net/10356/151857 |
| _version_ | 1811682867086884864 |
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| author | Urooj, Shabana Singh, Satya P. Malibari, Areej Alrowais, Fadwa Kalathil, Shaeen |
| author2 | Lee Kong Chian School of Medicine (LKCMedicine) |
| author_facet | Lee Kong Chian School of Medicine (LKCMedicine) Urooj, Shabana Singh, Satya P. Malibari, Areej Alrowais, Fadwa Kalathil, Shaeen |
| author_sort | Urooj, Shabana |
| collection | NTU |
| description | Effective and accurate diagnosis of Alzheimer’s disease (AD), as well as early-stage detection, has gained more and more attention in recent years. For AD classification, we propose a new hybrid method for early detection of Alzheimer’s disease (AD) using Polar Harmonic Transforms (PHT) and Self-adaptive Differential Evolution Wavelet Neural Network (SaDE-WNN). The orthogonal moments are used for feature extraction from the grey matter tissues of structural Magnetic Resonance Imaging (MRI) data. Irrelevant features are removed by the feature selection process through evaluating the in-class and among-class variance. In recent years, WNNs have gained attention in classification tasks; however, they suffer from the problem of initial parameter tuning, parameter setting. We proposed a WNN with the self-adaptation technique for controlling the Differential Evolution (DE) parameters, i.e., the mutation scale factor (F) and the cross-over rate (CR). Experimental results on the Alzheimer’s disease Neuroimaging Initiative (ADNI) database indicate that the proposed method yields the best overall classification results between AD and mild cognitive impairment (MCI) (93.7% accuracy, 86.0% sensitivity, 98.0% specificity, and 0.97 area under the curve (AUC)), MCI and healthy control (HC) (92.9% accuracy, 95.2% sensitivity, 88.9% specificity, and 0.98 AUC), and AD and HC (94.4% accuracy, 88.7% sensitivity, 98.9% specificity and 0.99 AUC). |
| first_indexed | 2024-10-01T04:03:39Z |
| format | Journal Article |
| id | ntu-10356/151857 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:03:39Z |
| publishDate | 2021 |
| record_format | dspace |
| spelling | ntu-10356/1518572023-03-05T16:49:52Z Early detection of alzheimer's disease using polar harmonic transforms and optimized wavelet neural network Urooj, Shabana Singh, Satya P. Malibari, Areej Alrowais, Fadwa Kalathil, Shaeen Lee Kong Chian School of Medicine (LKCMedicine) Science::Medicine Alzheimer’s Disease Discriminative Analysis Effective and accurate diagnosis of Alzheimer’s disease (AD), as well as early-stage detection, has gained more and more attention in recent years. For AD classification, we propose a new hybrid method for early detection of Alzheimer’s disease (AD) using Polar Harmonic Transforms (PHT) and Self-adaptive Differential Evolution Wavelet Neural Network (SaDE-WNN). The orthogonal moments are used for feature extraction from the grey matter tissues of structural Magnetic Resonance Imaging (MRI) data. Irrelevant features are removed by the feature selection process through evaluating the in-class and among-class variance. In recent years, WNNs have gained attention in classification tasks; however, they suffer from the problem of initial parameter tuning, parameter setting. We proposed a WNN with the self-adaptation technique for controlling the Differential Evolution (DE) parameters, i.e., the mutation scale factor (F) and the cross-over rate (CR). Experimental results on the Alzheimer’s disease Neuroimaging Initiative (ADNI) database indicate that the proposed method yields the best overall classification results between AD and mild cognitive impairment (MCI) (93.7% accuracy, 86.0% sensitivity, 98.0% specificity, and 0.97 area under the curve (AUC)), MCI and healthy control (HC) (92.9% accuracy, 95.2% sensitivity, 88.9% specificity, and 0.98 AUC), and AD and HC (94.4% accuracy, 88.7% sensitivity, 98.9% specificity and 0.99 AUC). Published version This research is funded by the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia. The project number is PNU-DRI-RI-20-019. 2021-10-20T00:21:16Z 2021-10-20T00:21:16Z 2021 Journal Article Urooj, S., Singh, S. P., Malibari, A., Alrowais, F. & Kalathil, S. (2021). Early detection of alzheimer's disease using polar harmonic transforms and optimized wavelet neural network. Applied Sciences, 11(4), 1574-. https://dx.doi.org/10.3390/app11041574 2076-3417 https://hdl.handle.net/10356/151857 10.3390/app11041574 2-s2.0-85100964598 4 11 1574 en Applied Sciences © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). application/pdf |
| spellingShingle | Science::Medicine Alzheimer’s Disease Discriminative Analysis Urooj, Shabana Singh, Satya P. Malibari, Areej Alrowais, Fadwa Kalathil, Shaeen Early detection of alzheimer's disease using polar harmonic transforms and optimized wavelet neural network |
| title | Early detection of alzheimer's disease using polar harmonic transforms and optimized wavelet neural network |
| title_full | Early detection of alzheimer's disease using polar harmonic transforms and optimized wavelet neural network |
| title_fullStr | Early detection of alzheimer's disease using polar harmonic transforms and optimized wavelet neural network |
| title_full_unstemmed | Early detection of alzheimer's disease using polar harmonic transforms and optimized wavelet neural network |
| title_short | Early detection of alzheimer's disease using polar harmonic transforms and optimized wavelet neural network |
| title_sort | early detection of alzheimer s disease using polar harmonic transforms and optimized wavelet neural network |
| topic | Science::Medicine Alzheimer’s Disease Discriminative Analysis |
| url | https://hdl.handle.net/10356/151857 |
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