KIDNEY STONE DETECTION USING MATLAB

Kidney stones, a prevalent urological disorder, pose a significant health risk globally. Timely detection and diagnosis are crucial for effective management and treatment. In recent years, convolutional neural networks (CNNs) have demonstrated remarkable capabilities in medical image analysis tasks. In this study, we propose a CNN-based approach for the automated detection of kidney stones from medical imaging data. Leveraging a dataset comprising a diverse range of kidney stone images, we design and train a deep CNN architecture capable of accurately identifying the presence of kidney stones. Our methodology involves preprocessing the dataset, designing the CNN architecture, training the model, and evaluating its performance using standard metrics. Through extensive experimentation and validation, we demonstrate the effectiveness and robustness of our proposed approach in accurately detecting kidney stones. Our findings underscore the potential of CNNs as a valuable tool in the early diagnosis and management of kidney stone-related disorders.