Image Based Detection of Craniofacial Abnormalities using Feature Extraction by Classical Convolutional Neural Network

Saloni Agarwal, Rami Robert Hallac, Rashika Mishra, Chao Li, Ovidiu Daescu, Alex A Kane

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish (US)
Title of host publication2018 IEEE 8th International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Volume2018-October
ISBN (Electronic)9781538685204
DOIs
StatePublished - Nov 20 2018
Event8th IEEE International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2018 - Las Vegas, United States
Duration: Oct 18 2018Oct 20 2018

Other

Other8th IEEE International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2018
CountryUnited States
CityLas Vegas
Period10/18/1810/20/18

Fingerprint

Pediatrics
Support vector machines
Feature extraction
Classifiers
Chemical activation
Neural networks

Keywords

  • AlexNet
  • Craniofacial
  • Craniosynostosis
  • multiclass SVM
  • Pediatric Cleft
  • Transfer Learning

ASJC Scopus subject areas

  • Biomedical Engineering
  • Computational Theory and Mathematics

Cite this

Agarwal, S., Hallac, R. R., Mishra, R., Li, C., Daescu, O., & Kane, A. A. (2018). Image Based Detection of Craniofacial Abnormalities using Feature Extraction by Classical Convolutional Neural Network. In 2018 IEEE 8th International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2018 (Vol. 2018-October). [8541948] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCABS.2018.8541948

Image Based Detection of Craniofacial Abnormalities using Feature Extraction by Classical Convolutional Neural Network. / Agarwal, Saloni; Hallac, Rami Robert; Mishra, Rashika; Li, Chao; Daescu, Ovidiu; Kane, Alex A.

2018 IEEE 8th International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2018. Vol. 2018-October Institute of Electrical and Electronics Engineers Inc., 2018. 8541948.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Agarwal, S, Hallac, RR, Mishra, R, Li, C, Daescu, O & Kane, AA 2018, Image Based Detection of Craniofacial Abnormalities using Feature Extraction by Classical Convolutional Neural Network. in 2018 IEEE 8th International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2018. vol. 2018-October, 8541948, Institute of Electrical and Electronics Engineers Inc., 8th IEEE International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2018, Las Vegas, United States, 10/18/18. https://doi.org/10.1109/ICCABS.2018.8541948
Agarwal S, Hallac RR, Mishra R, Li C, Daescu O, Kane AA. Image Based Detection of Craniofacial Abnormalities using Feature Extraction by Classical Convolutional Neural Network. In 2018 IEEE 8th International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2018. Vol. 2018-October. Institute of Electrical and Electronics Engineers Inc. 2018. 8541948 https://doi.org/10.1109/ICCABS.2018.8541948
Agarwal, Saloni ; Hallac, Rami Robert ; Mishra, Rashika ; Li, Chao ; Daescu, Ovidiu ; Kane, Alex A. / Image Based Detection of Craniofacial Abnormalities using Feature Extraction by Classical Convolutional Neural Network. 2018 IEEE 8th International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2018. Vol. 2018-October Institute of Electrical and Electronics Engineers Inc., 2018.
@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, multiclass SVM, Pediatric Cleft, Transfer Learning",
author = "Saloni Agarwal and Hallac, {Rami Robert} and Rashika Mishra and Chao Li and Ovidiu Daescu and Kane, {Alex A}",
year = "2018",
month = "11",
day = "20",
doi = "10.1109/ICCABS.2018.8541948",
language = "English (US)",
volume = "2018-October",
booktitle = "2018 IEEE 8th International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
address = "United States",

}

TY - GEN

T1 - Image Based Detection of Craniofacial Abnormalities using Feature Extraction by Classical Convolutional Neural Network

AU - Agarwal, Saloni

AU - Hallac, Rami Robert

AU - Mishra, Rashika

AU - Li, Chao

AU - Daescu, Ovidiu

AU - Kane, Alex A

PY - 2018/11/20

Y1 - 2018/11/20

N2 - 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.

AB - 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.

KW - AlexNet

KW - Craniofacial

KW - Craniosynostosis

KW - multiclass SVM

KW - Pediatric Cleft

KW - Transfer Learning

UR - http://www.scopus.com/inward/record.url?scp=85059766175&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85059766175&partnerID=8YFLogxK

U2 - 10.1109/ICCABS.2018.8541948

DO - 10.1109/ICCABS.2018.8541948

M3 - Conference contribution

AN - SCOPUS:85059766175

VL - 2018-October

BT - 2018 IEEE 8th International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2018

PB - Institute of Electrical and Electronics Engineers Inc.

ER -