Improved Detection of Kidney Stones and Fractures Using YOLO and CNN: A Comparative Study with Older Model

The accurate and early detection of medical conditions like kidney stones and bone fractures is critical for effective treatment. In this study, we propose an enhanced detection approach using a combination of YOLO (You Only Look Once) and Convolutional Neural Networks (CNNs). These models are evaluated against older, traditional detection algorithms in terms of precision, recall, and computational efficiency. By utilizing YOLO’s real-time object detection capabilities and CNN’s feature extraction power, we aim to significantly improve diagnostic accuracy for both conditions. Our dataset includes medical imaging data of kidney stones and fractures, pre-processed to optimize model training and validation. A comparative analysis reveals the advantages and limitations of our method compared to the older models, highlighting improvements in detection speed, accuracy, and robustness across varying image qualities. This research demonstrates the potential of integrating YOLO and CNN for enhanced medical diagnostics and paves the way for future improvements in medical imaging technologies.