Optimized TSA ResNet Architecture with TSH—Discriminatory Features for Kidney Stone Classification from QUS Images-Reference-Cited by-同舟云学术

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Optimized TSA ResNet Architecture with TSH—Discriminatory Features for Kidney Stone Classification from QUS Images

Published:2023 Issue: Volume: Page:227-245
ISSN:1860-949X
Container-title:Enabling Person-Centric Healthcare Using Ambient Assistive Technology
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Author:

Nagaraj P.,Muneeswaran V.,Jeyanathan Josephine Selle,Panda Baidyanath,Bhoi Akash Kumar

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-38281-9_10

Reference35 articles.

1. Edvardsson, V. O., Indridason, O. S., Haraldsson, G., Kjartansson, O., & Palsson, R. (2013). Temporal trends in the incidence of kidney stone disease. Kidney International, 83(1), 146–152.

2. Kumar, K., & Abhishek, B. (2012). Artificial neural networks for diagnosis of kidney stones disease (Vol. 10). GRIN Verlag.

3. Serrat, J., Lumbreras, F., Blanco, F., Valiente, M., & López-Mesas, M. (2017). myStone: A system for automatic kidney stone classification. Expert Systems with Applications, 89, 41–51.

4. Howles, S. A., & Thakker, R. V. (2020). Genetics of kidney stone disease. Nature Reviews Urology, 17(7), 407–421.

5. Schaeffer, A. J., Feng, Z., Trock, B. J., Mathews, R. I., Neu, A. M., Gearhart, J. P., & Matlaga, B. R. (2011). Medical comorbidities associated with pediatric kidney stone disease. Urology, 77(1), 195–199.

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