Abstract
The ubiquitous approach of transfer learning for feature extraction is harnessed for image based detection of two types of craniofacial abnormalities: pediatric cleft and craniosynostosis. In the current study, using features extracted from pre-Trained AlexNet activations, we train a multi class support vector machine (SVM) for cleft lip abnormality and developed a multi-view classifier using max voting for craniosynostosis anomaly detection. We achieved Area under the ROC curve (AUC) value of 0.95 for cleft abnormality and 0.84 for craniosynostosis.
Original language | English (US) |
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Title of host publication | 2018 IEEE 8th International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2018 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Volume | 2018-October |
ISBN (Electronic) | 9781538685204 |
DOIs | |
State | Published - Nov 20 2018 |
Event | 8th IEEE International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2018 - Las Vegas, United States Duration: Oct 18 2018 → Oct 20 2018 |
Other
Other | 8th IEEE International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2018 |
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Country | United States |
City | Las Vegas |
Period | 10/18/18 → 10/20/18 |
Keywords
- AlexNet
- Craniofacial
- Craniosynostosis
- multiclass SVM
- Pediatric Cleft
- Transfer Learning
ASJC Scopus subject areas
- Biomedical Engineering
- Computational Theory and Mathematics