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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Spedding, A.L. Di Fatta, G. Saddy, J.D. |
| Copyright Year | 2015 |
| Description | Author affiliation: Sch. of Syst. Eng., Univ. of Reading, Reading, UK (Spedding, A.L.; Di Fatta, G.) || Centre for Integrative Neurosci. & Neurodynamics, Univ. of Reading, Reading, UK (Saddy, J.D.) |
| Abstract | In this paper a custom classification algorithm based on linear discriminant analysis and probability-based weights is implemented and applied to the hippocampus measurements of structural magnetic resonance images from healthy subjects and Alzheimer's Disease sufferers; and then attempts to diagnose them as accurately as possible. The classifier works by classifying each measurement of a hippocampal volume as healthy control-sized or Alzheimer's Disease-sized, these new features are then weighted and used to classify the subject as a healthy control or suffering from Alzheimer's Disease. The preliminary results obtained reach an accuracy of 85.8% and this is a similar accuracy to state-of-the-art methods such as a Naive Bayes classifier and a Support Vector Machine. An advantage of the method proposed in this paper over the aforementioned state-of-the-art classifierst is the descriptive ability of the classifications it produces. The descriptive model can be of great help to aid a doctor in the diagnosis of Alzheimer's Disease, or even further the understand of how Alzheimer's Disease affects the hippocampus. |
| Sponsorship | IEEE |
| Starting Page | 1404 |
| Ending Page | 1411 |
| File Size | 1487878 |
| Page Count | 8 |
| File Format | |
| e-ISBN | 9781467367998 |
| DOI | 10.1109/BIBM.2015.7359883 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2015-11-09 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Neuroimaging Classification algorithms |
| Content Type | Text |
| Resource Type | Article |
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