Identification of kidney stone types by deep learning integrated with radiomics features.
10.7507/1001-5515.202310043
- Author:
Chao SUN
1
;
Jun NI
1
;
Jianhe LIU
2
;
Huafeng LI
3
;
Dapeng TAO
1
Author Information
1. School of Information, Yunnan University, Kunming 650500, P. R. China.
2. Department of Urology, The Second Affiliated Hospital of Kunming Medical University, Kunming 650500, P. R. China.
3. Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, P. R. China.
- Publication Type:Journal Article
- Keywords:
Deep learning;
Image classification;
Infectious kidney stones;
Machine learning;
Radiomics
- MeSH:
Humans;
Kidney Calculi/classification*;
Deep Learning;
Neural Networks, Computer;
Tomography, X-Ray Computed;
Imaging, Three-Dimensional;
Radiomics
- From:
Journal of Biomedical Engineering
2024;41(6):1213-1220
- CountryChina
- Language:Chinese
-
Abstract:
Currently, the types of kidney stones before surgery are mainly identified by human beings, which directly leads to the problems of low classification accuracy and inconsistent diagnostic results due to the reliance on human knowledge. To address this issue, this paper proposes a framework for identifying types of kidney stones based on the combination of radiomics and deep learning, aiming to achieve automated preoperative classification of kidney stones with high accuracy. Firstly, radiomics methods are employed to extract radiomics features released from the shallow layers of a three-dimensional (3D) convolutional neural network, which are then fused with the deep features of the convolutional neural network. Subsequently, the fused features are subjected to regularization, least absolute shrinkage and selection operator (LASSO) processing. Finally, a light gradient boosting machine (LightGBM) is utilized for the identification of infectious and non-infectious kidney stones. The experimental results indicate that the proposed framework achieves an accuracy rate of 84.5% for preoperative identification of kidney stone types. This framework can effectively distinguish between infectious and non-infectious kidney stones, providing valuable assistance in the formulation of preoperative treatment plans and the rehabilitation of patients after surgery.
