Title |
Automated identification of dementia using medical imaging: a survey from a pattern classification perspective
|
---|---|
Published in |
Brain Informatics, December 2015
|
DOI | 10.1007/s40708-015-0027-x |
Pubmed ID | |
Authors |
Chuanchuan Zheng, Yong Xia, Yongsheng Pan, Jinhu Chen |
Abstract |
In this review paper, we summarized the automated dementia identification algorithms in the literature from a pattern classification perspective. Since most of those algorithms consist of both feature extraction and classification, we provide a survey on three categories of feature extraction methods, including the voxel-, vertex- and ROI-based ones, and four categories of classifiers, including the linear discriminant analysis, Bayes classifiers, support vector machines, and artificial neural networks. We also compare the reported performance of many recently published dementia identification algorithms. Our comparison shows that many algorithms can differentiate the Alzheimer's disease (AD) from elderly normal with a largely satisfying accuracy, whereas distinguishing the mild cognitive impairment from AD or elderly normal still remains a major challenge. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 1 | 1% |
Unknown | 83 | 99% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 13 | 15% |
Researcher | 11 | 13% |
Student > Master | 11 | 13% |
Student > Bachelor | 6 | 7% |
Lecturer | 5 | 6% |
Other | 13 | 15% |
Unknown | 25 | 30% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 18 | 21% |
Engineering | 15 | 18% |
Psychology | 4 | 5% |
Agricultural and Biological Sciences | 4 | 5% |
Neuroscience | 4 | 5% |
Other | 10 | 12% |
Unknown | 29 | 35% |