Deep Learning Techniques for Kidney Stone Detection and Classification (KEY IJP************162)

• Ravi

Abstract

This study proposes an ultrasound speckle suppression method for detecting kidney stones in humans. The process begins with image enhancement techniques to adjust the image intensities, followed by the application of median filters to smooth the image and remove noise. The preprocessed images are then segmented using a thresholding technique. The median filter effectively removes impulsive noise, such as salt-and-pepper noise. The proposed method identifies kidney stones by pinpointing their location coordinates. In recent years, deep learning has become a critical approach for detecting and classifying kidney stones, particularly through the analysis of CT scan images. This project aims to develop a robust system for kidney stone detection by leveraging deep learning algorithms to accurately classify images as either affected by kidney stones or not. The system uses a dataset of kidney CT scan images in common formats like .png and .jpg, which are preprocessed for model training. To ensure high accuracy, two deep learning architectures are explored: Convolutional Neural Networks (CNN) and a hybrid model combining CNN with RESNET-50, a pre-trained network known for its strong image recognition performance. The CNN model employs 2D convolution layers to extract hierarchical features from the images, while the CNN-RESNET-50 hybrid model enhances feature extraction through residual learning, addressing challenges like vanishing gradients in image classification. Both models are trained on the dataset, and their effectiveness is evaluated based on their ability to detect kidney stones in CT scans. The system ultimately provides a prediction indicating whether the input CT scan shows signs of kidney stones, serving as a reliable, automated diagnostic tool for healthcare professionals. By utilizing these deep learning techniques, the approach aims to improve the accuracy, speed, and accessibility of kidney stone detection, contributing to earlier diagnosis and better patient outcomes. Through comprehensive training, validation, and testing, this system demonstrates the potential of artificial intelligence to revolutionize medical imaging and kidney stone detection