A Comparative Study on Various Classification models/ network Including XResnet-50, ExDark19, CystoNet, CNN, ANN in Kidney Stone Detection Using Deep Machine Learning
Keywords:
Kidney Stone Detection, CNN, ANN, XResNet-50, ExDark19, CystoNet, Deep Learning, Kidney Stone Detection, and Medical Imaging.Abstract
In medical imaging, kidney stone detection is a crucial task that necessitates precise and effective deep learning models for diagnosis. In order to assess the efficacy of differentdeep-learnin g architectures in kidney stone detection, we compare and contrast XResNet-50,
ExDark19, CystoNet, Convolutional Neural Networks (CNN), and Artificial Neural Networks (ANN). Using a standardized kidney stone dataset, the models are evaluated according to important performance measures like accuracy, precision, recall, and F1-score.
References
Sulaksono, N., Adi, K., &Isnanto, D. R. (2023). Deep Learning on Medical Imaging in Identifying Kidney Stones. E3S Web of Conferences, 448, 02019. https://doi.org/10.1051/e3sconf/202344802019
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Published
2024-04-23
How to Cite
Sandeep Lather,Dr. Sandeep. (2024). A Comparative Study on Various Classification models/ network Including XResnet-50, ExDark19, CystoNet, CNN, ANN in Kidney Stone Detection Using Deep Machine Learning. Journal of Computational Analysis and Applications (JoCAAA), 33(4), 665–680. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/2395
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