A Comprehensive Multi-Model Approach to Kidney Stone Detection using Deep Learning

Abstract: This work presents an end-to-end deep learning-based kidney stone detection system based on a combination of Convolutional Neural Networks (CNNs) for ultrasound image processing and Random Forest for clinical data classification. The combination of the two models has an enormously improved diagnostic accuracy, reducing misclassification rates. With the use of a fusion model, the system effectively facilitates decision-making with accurate and autonomous detection. This study illustrates the advantages of multi-modal learning in medical diagnosis and aims to support healthcare professionals in early detection and treatment planning.