On the In Vivo Recognition of Kidney Stones Using Machine Learning
Determining the type of kidney stones allows urologists to prescribe a treatment to avoid the recurrence of renal lithiasis. An automated in-vivo image-based classification method would be an important step towards an immediate identification of the kidney stone type required as a first phase of the...
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| Main Authors: | Francisco Lopez-Tiro, Vincent Estrade, Jacques Hubert, Daniel Flores-Araiza, Miguel Gonzalez-Mendoza, Gilberto Ochoa, Christian Daul |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10384337/ |
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