Liver Cancer Classification Using Single Pass Neural Networks Based on Ultrasound Images: A Review
Author:
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Computer Science Applications
Link
https://link.springer.com/content/pdf/10.1007/s11277-023-10283-w.pdf
Reference77 articles.
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2. Rhyou, S. Y., & Yoo, J. C. (2021). Cascaded deep learning neural network for automated liver steatosis diagnosis using ultrasound images. Sensors. https://doi.org/10.3390/s21165304
3. Brehar, R., Mitrea, D. A., Vancea, F., Marita, T., Nedevschi, S., Lupsor-Platon, M., & Badea, R. I. (2020). Comparison of deep-learning and conventional machine-learning methods for the automatic recognition of the hepatocellular carcinoma areas from ultrasound images. Sensors (Switzerland), 20(11), 1–22. https://doi.org/10.3390/s20113085
4. Bartlett, P. L. (1998). The sample complexity of pattern classification with neural networks: The size of the weights is more important than the size of the network. IEEE Transactions on Information Theory, 44(2), 525–536. https://doi.org/10.1109/18.661502
5. Wang, X., & Cao, W. (2018). Non-iterative approaches in training feed-forward neural networks and their applications. Soft Computing, 22(11), 3473–3476. https://doi.org/10.1007/S00500-018-3203-0
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