Classifying Fetal Health Using Neural Networks by Boosting Imbalanced Classes
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-3734-9_28
Reference22 articles.
1. Akbulut A, Ertugrul E, Topcu V (2018) Fetal health status prediction based on maternal clinical history using machine learning techniques. Comput Methods Programs Biomed 163:87–100
2. Miao JH, Miao KH (2018) Cardiotocographic diagnosis of fetal health based on multiclass morphologic pattern predictions using deep learning classification. Int J Adv Comput Sci Appl 9(5)
3. Zhao X, Zeng X, Koehl L, Tartare G, de Jonckheere J, Song K (2019) An IoT-based wearable system using accelerometers and machine learning for fetal movement monitoring. In: 2019 IEEE international conference on industrial cyber physical systems (ICPS). IEEE, pp 299–304
4. Wang G, Li W,. Zuluaga MA, Pratt R, Patel PA, Aertsen M, Doel T et al (2018) Interactive medical image segmentation using deep learning with image-specific fine tuning. IEEE Trans Med Imaging 37(7):1562–1573
5. Li J, Liu X (2021) Fetal health classification based on machine learning. In: 2021 IEEE 2nd international conference on big data, artificial intelligence and internet of things engineering (ICBAIE). IEEE, pp 899–902
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