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Early kidney stone detection is essential for the diagnosis and treatment of people who have kidney stones. The objective of this study is to employ deep learning algorithms for renal stone detection, addressing the critical need for early, accurate diagnosis, which can significantly improve patient outcomes and reduce healthcare costs. The paper thoroughly assesses a variety of models, including ResNet, DenseNet, and EfficientNet, for CT images. The limitations of manual identification procedures highlight the urgent need for a more effective automated approach, making this research necessary. Notably, the painstakingly improved DenseNet model achieves a peak accuracy of 0.86, demonstrating its potential superiority. These results convincingly demonstrate the revolutionary power of deep learning, which is poised to revolutionise the detection of renal stones. This fast, trustworthy, and non-invasive method has the potential to advance clinical procedures and significantly improve patient care. |
