Design of deep learning reaction–diffusion level set segmentation approach for health care related to automatic kidney stone detection analysis
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Media Technology,Software
Link
https://link.springer.com/content/pdf/10.1007/s11042-021-11263-7.pdf
Reference44 articles.
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2. Al-Asad JF, Moghadamjoo A (2010) Short-time fourier transform and Wigner-Ville transform for ultrasound image de-noising through dynamic mask thresholding. Bioinf Biomed Eng. https://doi.org/10.1109/ICBBE.2010.5517751
3. Anderrson T, Lathen G (2013) Modified gradient search for level set based image segmentation. IEEE Trans Image Process 22(2):621–630
4. Anushalin PS, Samson IJ (2014) Ultrasound image analysis of kidney stone using wavelet transform. Int J BioSci Eng 1(1):39–44
5. Bagly DH, Healy KA (2012) Ureteroscopic treatment of larger renal calculi (>2cm). Arab J Urol 10:296–300
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2. Transforming urinary stone disease management by artificial intelligence-based methods: A comprehensive review;Asian Journal of Urology;2023-07
3. Optimizing Kidney Stone Prediction through Urinary Analysis with Improved Binary Particle Swarm Optimization and eXtreme Gradient Boosting;Mathematics;2023-04-03
4. Self‐attention based progressive generative adversarial network optimized with arithmetic optimization algorithm for kidney stone detection;Concurrency and Computation: Practice and Experience;2023-01-10
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