Automated Kidney Stone Detection and Prediction Using Enhanced Deep Learning Models
DOI:
https://doi.org/10.48047/z9s0d127Keywords:
Kidney Stone Detection, Convolutional Neural Networks (CNN), Ultrasound Imaging, Medical Imaging, Risk AssessmentAbstract
This research introduces an advanced kidney stone detection system that employs Convolutional Neural Networks (CNNs) to improve diagnostic precision and expedite medical responses. The system was trained on a comprehensive dataset comprising 8,755 ultrasound images, categorized into 4,341 images representing kidney stone cases and 4,414 normal images. The CNN framework enables the automatic extraction of relevant features, allowing for effective identification and classification of kidney stones based on their size and positional context. With an accuracy of approximately 97%, along with favourable precision and recall metrics, the model demonstrates its reliability in clinical applications.
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References
Akmal, M., Alshahrani, M. M., & Alshahrani, S. (2022). Advances in the management of kidney stone A review. Clinical Medicine Insights: Urology, 16.
https://doi.org/10.1177/1179596X221090056 using
Eshghi, P., Mavrakis, P., & An, G. (2022). Deep learning for kidney stone diagnosis and classification CT images. Health Informatics Journal, 28(1), 146-157. https://doi.org/10.1177/14604582211096572
Esteva, A., Madani, A., & Khosravi, P. (2022). Applications of artificial intelligence in urology: A review. Urology, 168, 54-61. https://doi.org/10.1016/j.urology.2022.05.029
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