Internet of Medical Things–based decision system for automated classification of Alzheimer’s using three-dimensional views of magnetic resonance imaging scans
Internet of Medical Things is a smart provision of medical services to patients interacting with the doctors in harmony to uplift healthcare facilities. It enables the automated diagnosis of diseases for patients in remote areas. Alzheimer’s disease is one of the most chronic diseases and the main c...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-03-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147719831186 |
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| _version_ | 1849694979911319552 |
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| author | Umair Khan Armughan Ali Salabat Khan Farhan Aadil Mehr Yahya Durrani Khan Muhammad Ran Baik Jong Weon Lee |
| author_facet | Umair Khan Armughan Ali Salabat Khan Farhan Aadil Mehr Yahya Durrani Khan Muhammad Ran Baik Jong Weon Lee |
| author_sort | Umair Khan |
| collection | DOAJ |
| description | Internet of Medical Things is a smart provision of medical services to patients interacting with the doctors in harmony to uplift healthcare facilities. It enables the automated diagnosis of diseases for patients in remote areas. Alzheimer’s disease is one of the most chronic diseases and the main cause of dementia in human beings. Dementia affects the patient by a process of gradual degeneration of the human brain and results in an inability to perform daily routine tasks and actions. An automated system needs to be developed, to classify the subject with dementia and to determine the prodromal stage of dementia. Considering such requirement, a fully automated classification system is proposed. The proposed algorithm works on the hybrid feature vector combining the textural, statistical, and shape features extracted from three-dimensional views. The feature length is reduced using principal component analysis and relevant features are extracted for classification. The proposed algorithm is tested for both binary and multi-class problems. The method achieves the average precision of 99.2% and 99.02% for binary and multi-class classifications, respectively. The results outperform the existing methods. The algorithm showed accurate results with the average computational time of 0.05 s per magnetic resonance imaging scan. |
| format | Article |
| id | doaj-art-9ba02740ddbc4c1c8df1eefa996f26f5 |
| institution | DOAJ |
| issn | 1550-1477 |
| language | English |
| publishDate | 2019-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-9ba02740ddbc4c1c8df1eefa996f26f52025-08-20T03:19:54ZengWileyInternational Journal of Distributed Sensor Networks1550-14772019-03-011510.1177/1550147719831186Internet of Medical Things–based decision system for automated classification of Alzheimer’s using three-dimensional views of magnetic resonance imaging scansUmair Khan0Armughan Ali1Salabat Khan2Farhan Aadil3Mehr Yahya Durrani4Khan Muhammad5Ran Baik6Jong Weon Lee7Computer Science Department, COMSATS University Islamabad, Attock, PakistanComputer Science Department, COMSATS University Islamabad, Attock, PakistanComputer Science Department, COMSATS University Islamabad, Attock, PakistanComputer Science Department, COMSATS University Islamabad, Attock, PakistanComputer Science Department, COMSATS University Islamabad, Attock, PakistanDepartment of Software, Sejong University, Seoul, KoreaDepartment of Computer Engineering, Convergence School of ICT, Honam University, Gwangju, KoreaDepartment of Software, Sejong University, Seoul, KoreaInternet of Medical Things is a smart provision of medical services to patients interacting with the doctors in harmony to uplift healthcare facilities. It enables the automated diagnosis of diseases for patients in remote areas. Alzheimer’s disease is one of the most chronic diseases and the main cause of dementia in human beings. Dementia affects the patient by a process of gradual degeneration of the human brain and results in an inability to perform daily routine tasks and actions. An automated system needs to be developed, to classify the subject with dementia and to determine the prodromal stage of dementia. Considering such requirement, a fully automated classification system is proposed. The proposed algorithm works on the hybrid feature vector combining the textural, statistical, and shape features extracted from three-dimensional views. The feature length is reduced using principal component analysis and relevant features are extracted for classification. The proposed algorithm is tested for both binary and multi-class problems. The method achieves the average precision of 99.2% and 99.02% for binary and multi-class classifications, respectively. The results outperform the existing methods. The algorithm showed accurate results with the average computational time of 0.05 s per magnetic resonance imaging scan.https://doi.org/10.1177/1550147719831186 |
| spellingShingle | Umair Khan Armughan Ali Salabat Khan Farhan Aadil Mehr Yahya Durrani Khan Muhammad Ran Baik Jong Weon Lee Internet of Medical Things–based decision system for automated classification of Alzheimer’s using three-dimensional views of magnetic resonance imaging scans International Journal of Distributed Sensor Networks |
| title | Internet of Medical Things–based decision system for automated classification of Alzheimer’s using three-dimensional views of magnetic resonance imaging scans |
| title_full | Internet of Medical Things–based decision system for automated classification of Alzheimer’s using three-dimensional views of magnetic resonance imaging scans |
| title_fullStr | Internet of Medical Things–based decision system for automated classification of Alzheimer’s using three-dimensional views of magnetic resonance imaging scans |
| title_full_unstemmed | Internet of Medical Things–based decision system for automated classification of Alzheimer’s using three-dimensional views of magnetic resonance imaging scans |
| title_short | Internet of Medical Things–based decision system for automated classification of Alzheimer’s using three-dimensional views of magnetic resonance imaging scans |
| title_sort | internet of medical things based decision system for automated classification of alzheimer s using three dimensional views of magnetic resonance imaging scans |
| url | https://doi.org/10.1177/1550147719831186 |
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