Design and Validation of a Deep Learning Model for Renal Stone Detection and Segmentation on Kidney–Ureter–Bladder Images

Autor:	Zih-Hao Huang, Yi-Yang Liu, Wei-Juei Wu, Ko-Wei Huang
Jazyk:	angličtina
Rok vydání:	2023
Předmět:	kidney–ureter–bladder images renal stones computer-aided diagnosis deep learning classification model semantic segmentation Technology Biology (General) QH301-705.5
Zdroj:	Bioengineering, Vol 10, Iss 8, p 970 (2023)
Druh dokumentu:	article
ISSN:	2306-5354
DOI:	10.3390/bioengineering10080970
Popis:	Kidney–ureter–bladder (KUB) imaging is used as a frontline investigation for patients with suspected renal stones. In this study, we designed a computer-aided diagnostic system for KUB imaging to assist clinicians in accurately diagnosing urinary tract stones. The image dataset used for training and testing the model comprised 485 images provided by Kaohsiung Chang Gung Memorial Hospital. The proposed system was divided into two subsystems, 1 and 2. Subsystem 1 used Inception-ResNetV2 to train a deep learning model on preprocessed KUB images to verify the improvement in diagnostic accuracy with image preprocessing. Subsystem 2 trained an image segmentation model using the ResNet hybrid, U-net, to accurately identify the contours of renal stones. The performance was evaluated using a confusion matrix for the classification model. We conclude that the model can assist clinicians in accurately diagnosing renal stones via KUB imaging. Therefore, the proposed system can assist doctors in diagnosis, reduce patients’ waiting time for CT scans, and minimize the radiation dose absorbed by the body.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/2a351c83f984491dab024b2d14dfd10e Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF Plný text ve formátu HTML
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