@inproceedings{620e02e604a346b0b1d185ad9d687ae8,
title = "Image Based Detection of Craniofacial Abnormalities using Feature Extraction by Classical Convolutional Neural Network",
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.",
keywords = "AlexNet, Craniofacial, Craniosynostosis, Pediatric Cleft, Transfer Learning, multiclass SVM",
author = "Saloni Agarwal and Hallac, {Rami R.} and Rashika Mishra and Chao Li and Ovidiu Daescu and Alex Kane",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 8th IEEE International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2018 ; Conference date: 18-10-2018 Through 20-10-2018",
year = "2018",
month = nov,
day = "20",
doi = "10.1109/ICCABS.2018.8541948",
language = "English (US)",
series = "IEEE International Conference on Computational Advances in Bio and Medical Sciences, ICCABS",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2018 IEEE 8th International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2018",
}