Abstract
The development of an objective algorithm to assess craniosynostosis has the potential to facilitate early diagnosis, especially for care providers with limited craniofacial expertise. In this study, we process multiview 2D images of infants with craniosynostosis and healthy controls by computer-based classifiers to identify disease. We develop two multiview image-based classifiers, first based on traditional machine learning (ML) with feature extraction, and the other one based on CNNs. The ML model performs slightly better (accuracy 91.7%) than the CNN model (accuracy 90.6%), likely due to the availability of a small image dataset for model training and superiority of the ML features in differentiation of craniosynostosis subtypes.
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Agarwal, S., Hallac, R.R., Daescu, O., Kane, A. (2021). Classification of Craniosynostosis Images by Vigilant Feature Extraction. In: Arabnia, H.R., Deligiannidis, L., Shouno, H., Tinetti, F.G., Tran, QN. (eds) Advances in Computer Vision and Computational Biology. Transactions on Computational Science and Computational Intelligence. Springer, Cham. https://doi.org/10.1007/978-3-030-71051-4_23
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