ABSTRACT
In order to optimize treatment and avoid severe kidney stone sickness, early detection and size quantification of renal calculi are crucial. When it comes to kidney stones, volummetric measurements are far more accurate and dependable than linear ones. Automating stone volume evaluation using deep learning-based algorithms and non-contrast CT scans of the abdomen can greatly save manual labour. Model training, segmentation, and preprocessing are the three key parts. In this case, the preprocessing that involves embossing is most accurately accomplished using histogram equalization. Using watershed segmentation, objects that are near one other can be detected and separated. For more accurate findings, we used the CNN-RELM to train the model. Compared to other methods, such as CNN and ELM, the proposed strategy achieved a higher accuracy of 97.56%.
