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

doi:10.1109/ICCABS.2018.8541948

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 proceeding › Conference contribution

4 Scopus citations

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)
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	https://doi.org/10.1109/ICCABS.2018.8541948
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
Country/Territory	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

Access to Document

10.1109/ICCABS.2018.8541948

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 et al.

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 proceeding › Conference 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 doi: 10.1109/ICCABS.2018.8541948

Agarwal, Saloni ; Hallac, Rami Robert ; Mishra, Rashika et al. / 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.

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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.",

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

Abstract

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Keywords

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