Kidney Stone Detection Using CNN

Diagnosing and treating kidney stones early on is essential to avoid serious consequences. An automated method for kidney stone diagnosis in medical imaging using Convolutional Neural Networks (CNNs) is proposed here. With its capacity to extract complex information, the CNN model is able to recognize patterns in ultrasound or CT scan pictures that are suggestive of kidney stones. By using a sizable dataset of annotated photos for training, the suggested system is able to efficiently generalize to new data. The kidney stone detection model, which lessens the need for human interpretation and streamlines the diagnosis procedure, achieves excellent accuracy of 97% through the use of deep learning techniques. Primary goal is to suggest a CNN-based technique for kidney stone diagnosis in medical imaging, such as ultrasounds, CT scans, and X-rays. The objective is to develop a precise and automated method that will improve clinical diagnostic performance and patient care.