On the in vivo recognition of kidney stones using machine learning
- URL: http://arxiv.org/abs/2201.08865v2
- Date: Thu, 24 Aug 2023 21:58:18 GMT
- Title: On the in vivo recognition of kidney stones using machine learning
- Authors: Francisco Lopez-Tiro, Vincent Estrade, Jacques Hubert, Daniel
Flores-Araiza, Miguel Gonzalez-Mendoza, Gilberto Ochoa-Ruiz, Christian Daul
- Abstract summary: This paper compares the kidney stone recognition performances of six shallow machine learning methods and three deep-learning architectures.
It is also shown that choosing an appropriate colour space and texture features allows a shallow machine learning method to approach closely the performances of the most promising deep-learning methods.
- Score: 1.6273083168563973
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Determining the type of kidney stones allows urologists to prescribe a
treatment to avoid 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 diagnosis. In the literature it was shown on ex-vivo data (i.e., in very
controlled scene and image acquisition conditions) that an automated kidney
stone classification is indeed feasible. This pilot study compares the kidney
stone recognition performances of six shallow machine learning methods and
three deep-learning architectures which were tested with in-vivo images of the
four most frequent urinary calculi types acquired with an endoscope during
standard ureteroscopies. This contribution details the database construction
and the design of the tested kidney stones classifiers. Even if the best
results were obtained by the Inception v3 architecture (weighted precision,
recall and F1-score of 0.97, 0.98 and 0.97, respectively), it is also shown
that choosing an appropriate colour space and texture features allows a shallow
machine learning method to approach closely the performances of the most
promising deep-learning methods (the XGBoost classifier led to weighted
precision, recall and F1-score values of 0.96). This paper is the first one
that explores the most discriminant features to be extracted from images
acquired during ureteroscopies.
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