| Issue |
E3S Web Conf.
Volume 448, 2023
The 8th International Conference on Energy, Environment, Epidemiology and Information System (ICENIS 2023)
|
|
|---|---|---|
| Article Number | 02019 | |
| Number of page(s) | 8 | |
| Section | Information System | |
| DOI | https://doi.org/10.1051/e3sconf/202344802019 | |
| Published online | 17 November 2023 | |
Deep Learning on Medical Imaging in Identifying Kidney Stones: Review Paper
1 Doctoral Program of Information System, School of Postgraduate Studies, Diponegoro University, Indonesia
2,3 School of Postgraduate Studies, Diponegoro University, Indonesia
4 Poltekkes Kemenkes Semarang
* Nanang Sulaksono : nanangsulaksono123@gmail.com
Medical imaging is currently using artificial intelligence-based technologies to aid evaluate diagnostic information images, particularly in enforcing kidney stones. Artificial intelligence technology continues to develop, many studies show that deep learning is more widely used compared to traditional machine learning, so an Artificial intelligence system is needed to assist the accuracy of health diagnoses, thus helping in the field of radiology health. The aim of the research is to use artificial intelligence with deep learning models to help detect abnormalities in the kidneys. This research method is a literature review of Scopus data related to deep learning in medical imaging in detecting kidney stones. The results of using Artificial Intelligence in medical imaging can be used in diagnosing diseases including detecting Covid-19, musculoskeletal, calcium scores on Cardiac CT, liver tumors, urinary tract lesions, examination of the abdomen and kidney stones. Utilization of Artificial Intelligence in detecting kidney stones can be done with various classification models including XResNet-50, ExDark19, CystoNet, CNN, ANN. Using the right model and having a high accuracy value can help radiologists to accurately detect kidney stones.
Key words: Artificial intelligence / Deep learning / Medical imaging / Kidney Stones
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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