Name
Title | ||
|---|---|---|
Mri Based Automated Diagnosis Of Alzheimer'S: Fusing 3d Wavelet-Features With Clinical Data |
Abstract | ||
|---|---|---|
This paper presents a novel algorithm for classification of patients with Alzheimer's disease (AD) and Mild Cognitive Impairment (MCI) from the healthy controls (HC) using structural MRI. Feature extraction is based on discrete 3D wavelet transform followed by PCA for transforming the feature space into linearly uncorrelated variables. Linear SVM is used for classification purposes with clinical dementia rating used as the target vector. Proposed methodology is fully automated and independent of the annotation of region of interest. The importance of MRI, demographical data, neuro-psychiatric test scores and statistics calculated over the wavelet coefficients for the classification is studied. Proposed methodology is applied on 1 9 7 subjects from a public database. A classification accuracy of 9 5 % was achieved for the case of HC vs AD. For the case of HC vs MCI, and MCI vs AD the classification accuracy of 7 8 % and 8 1 % were achieved. The results are compared with an existing state of the art technique. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/EMBC.2017.8037048 | 2017 39TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) |
Field | DocType | Volume |
Clinical Dementia Rating,Feature vector,Pattern recognition,Computer science,Uncorrelated,Feature extraction,Artificial intelligence,Region of interest,Linear svm,Wavelet transform,Wavelet | Conference | 2017 |
ISSN | Citations | PageRank |
1094-687X | 0 | 0.34 |
References | Authors | |
1 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Aymen Ayaz | 1 | 0 | 0.34 |
| Muhammad Zubair | 2 | 45 | 16.50 |
| Khawar Khurshid | 3 | 8 | 3.84 |
| Awais M. Kamboh | 4 | 26 | 7.01 |