Name
Abstract | ||
|---|---|---|
This paper proposes a parallel machine learning framework for detecting Alzheimer's disease through T1-weighted MRI scans localised to the hippocampus, segmented between the left and right hippocampi. Feature extraction is first performed by 2 separately trained, unsupervised learning based AutoEncoders, where the left and right hippocampi are fed into their respective AutoEncoder. Classification is then performed by a pair of classifiers on the encoded data from the AutoEncoders, to which each pair of the classifiers are aggregated together using a soft voting ensemble process. The best averaged aggregated model results recorded was with the Gaussian Naive Bayes classifier where sensitivity/specificity achieved were 80%/81% respectively and a balanced accuracy score of 80%. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/978-3-030-86993-9_38 | BRAIN INFORMATICS, BI 2021 |
Keywords | DocType | Volume |
Machine learning, Alzheimer's disease, Autoencoder, Multi-layer perceptron, Support vector machine, Gaussian Naive Bayes | Conference | 12960 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sean A. Knox | 1 | 0 | 0.34 |
| Tianhua Chen | 2 | 42 | 7.16 |
| Pan Su | 3 | 82 | 11.72 |
| Grigoris Antoniou | 4 | 0 | 0.34 |