myStone: A system for automatic kidney stone classification

作者：

Highlights：

• First attempt at automatic classification of kidney stones.

• Construction of a device for the visual recognition of renal calculi.

• First extensive dataset of kidney stone images of 908 fragments.

• Design of features and classifier attaining 63% accuracy.

• A boost in performance is possible with the use of the urinary pH level.

摘要

•First attempt at automatic classification of kidney stones.•Construction of a device for the visual recognition of renal calculi.•First extensive dataset of kidney stone images of 908 fragments.•Design of features and classifier attaining 63% accuracy.•A boost in performance is possible with the use of the urinary pH level.

论文关键词：Kidney stone,Optical device,Computer vision,Image classification

论文评审过程：Received 5 April 2017, Revised 14 July 2017, Accepted 15 July 2017, Available online 17 July 2017, Version of Record 22 July 2017.

论文官网地址：https://doi.org/10.1016/j.eswa.2017.07.024