An intelligent Alzheimer’s disease diagnosis method using unsupervised feature learning
Abstract Today, the diagnosis of Alzheimer’s disease (AD) or mild cognitive impairment (MCI) has attracted the attention of researchers in this field owing to the increase in the occurrence of the diseases and the need for early diagnosis. Unfortunately, the nature of high dimension of neural data a...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2019-04-01
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| Series: | Journal of Big Data |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s40537-019-0190-7 |
| _version_ | 1818174674633228288 |
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| author | Firouzeh Razavi Mohammad Jafar Tarokh Mahmood Alborzi |
| author_facet | Firouzeh Razavi Mohammad Jafar Tarokh Mahmood Alborzi |
| author_sort | Firouzeh Razavi |
| collection | DOAJ |
| description | Abstract Today, the diagnosis of Alzheimer’s disease (AD) or mild cognitive impairment (MCI) has attracted the attention of researchers in this field owing to the increase in the occurrence of the diseases and the need for early diagnosis. Unfortunately, the nature of high dimension of neural data and few available samples led to the creation of a precise computer diagnostic system. Machine learning techniques, especially deep learning, have been considered as a useful tool in this field. Inspired by the concept of unsupervised feature learning that uses artificial intelligence to learn features from raw data, a two-stage method was presented for an intelligent diagnosis of Alzheimer’s disease. At the first stage of learning, scattered filtering, an uncontrolled two-layer neural network was used to directly learn features from raw data. At the second stage, SoftMax regression was used to categorize health statuses based on the learned features. The proposed method was validated by the data sets of Alzheimer’s Brain Images. The results showed that the proposed method achieved very good diagnostic accuracy and was better than the existing methods for brain image data sets. The proposed method reduces the need for human work and makes it easy to intelligently diagnose for big data processing, because the learning features are adaptive. In our experiments with the Alzheimer’s Disease Neuroimaging Initiative (ADNI) data, a dual and multi-class classification was conducted for AD/MCI diagnosis and the superiority of the proposed method in comparison with the advanced methods was shown. |
| first_indexed | 2024-12-11T19:48:09Z |
| format | Article |
| id | doaj.art-3cf0bcadff4542cea8ea60cb8a8d196a |
| institution | Directory Open Access Journal |
| issn | 2196-1115 |
| language | English |
| last_indexed | 2024-12-11T19:48:09Z |
| publishDate | 2019-04-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Big Data |
| spelling | doaj.art-3cf0bcadff4542cea8ea60cb8a8d196a2022-12-22T00:52:51ZengSpringerOpenJournal of Big Data2196-11152019-04-016111610.1186/s40537-019-0190-7An intelligent Alzheimer’s disease diagnosis method using unsupervised feature learningFirouzeh Razavi0Mohammad Jafar Tarokh1Mahmood Alborzi2Department of Information Technology Management, Islamic Azad University, Science and Research Branch of TehranDepartment of Industrial Engineering, K.N. Toosi University of TechnologyDepartment of Information Technology Management, Islamic Azad University, Science and Research Branch of TehranAbstract Today, the diagnosis of Alzheimer’s disease (AD) or mild cognitive impairment (MCI) has attracted the attention of researchers in this field owing to the increase in the occurrence of the diseases and the need for early diagnosis. Unfortunately, the nature of high dimension of neural data and few available samples led to the creation of a precise computer diagnostic system. Machine learning techniques, especially deep learning, have been considered as a useful tool in this field. Inspired by the concept of unsupervised feature learning that uses artificial intelligence to learn features from raw data, a two-stage method was presented for an intelligent diagnosis of Alzheimer’s disease. At the first stage of learning, scattered filtering, an uncontrolled two-layer neural network was used to directly learn features from raw data. At the second stage, SoftMax regression was used to categorize health statuses based on the learned features. The proposed method was validated by the data sets of Alzheimer’s Brain Images. The results showed that the proposed method achieved very good diagnostic accuracy and was better than the existing methods for brain image data sets. The proposed method reduces the need for human work and makes it easy to intelligently diagnose for big data processing, because the learning features are adaptive. In our experiments with the Alzheimer’s Disease Neuroimaging Initiative (ADNI) data, a dual and multi-class classification was conducted for AD/MCI diagnosis and the superiority of the proposed method in comparison with the advanced methods was shown.http://link.springer.com/article/10.1186/s40537-019-0190-7Alzheimer’s diseaseSparse filteringUnsupervised feature learningIntelligent diagnosisSoftMax regression |
| spellingShingle | Firouzeh Razavi Mohammad Jafar Tarokh Mahmood Alborzi An intelligent Alzheimer’s disease diagnosis method using unsupervised feature learning Journal of Big Data Alzheimer’s disease Sparse filtering Unsupervised feature learning Intelligent diagnosis SoftMax regression |
| title | An intelligent Alzheimer’s disease diagnosis method using unsupervised feature learning |
| title_full | An intelligent Alzheimer’s disease diagnosis method using unsupervised feature learning |
| title_fullStr | An intelligent Alzheimer’s disease diagnosis method using unsupervised feature learning |
| title_full_unstemmed | An intelligent Alzheimer’s disease diagnosis method using unsupervised feature learning |
| title_short | An intelligent Alzheimer’s disease diagnosis method using unsupervised feature learning |
| title_sort | intelligent alzheimer s disease diagnosis method using unsupervised feature learning |
| topic | Alzheimer’s disease Sparse filtering Unsupervised feature learning Intelligent diagnosis SoftMax regression |
| url | http://link.springer.com/article/10.1186/s40537-019-0190-7 |
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