The generated dataset consists of 630 DUSX images obtained from patients who applied to Atatürk University, Urology Department due to urinary system kidney stone disease. In the dataset, 558 images have one or more kidney stones in different regions with various sizes. The rest 72 images does not include a stone and they are labeled as healthy kidneys.
If you find this repo useful in your research, please consider citing:
@article{kilic2023exploring,
title={Exploring the Effect of Image Enhancement Techniques with Deep Neural Networks on Direct Urinary System (DUSX) Images for Automated Kidney Stone Detection},
author={Kilic, Ugur and Karabey Aksakalli, Isil and Tumuklu Ozyer, Gulsah and Aksakalli, Tugay and Ozyer, Baris and Adanur, Senol},
journal={International Journal of Intelligent Systems},
volume={2023},
number={1},
pages={3801485},
year={2023},
publisher={Wiley Online Library}
}