Abstract
Renal calculi alias kidney stone is a prevalent aching disease. Approximately 15% of the global population is a victim of this illness. Identifying the presence of the stones in the initial stages is a tedious and challenging task. The contemporary solutions for this problem fail to accurately detect the location and measurements of the stone. This project aims at providing a potential solution to the above problem. A combination of CNN models namely XResNet and FasterRCNN is proposed in this paper. The position and measurements of the stones are identified with high accuracy in the Region of Interest of the CT scan images. This helps doctors to suggest appropriate treatment without further ado.
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Madhavi, A., Harshitha, M., Deepak Sai, M., Anand, N. (2023). Detection of Renal Calculi Using Convolutional Neural Networks. In: Asari, V.K., Singh, V., Rajasekaran, R., Patel, R.B. (eds) Computational Methods and Data Engineering. Lecture Notes on Data Engineering and Communications Technologies, vol 139. Springer, Singapore. https://doi.org/10.1007/978-981-19-3015-7_7
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