Automatic Kidney Stone Detection Using Deep learning Method

Abstract

Kidney stone disease is a common urological illness that affects millions of people worldwide. The identification of kidney stones early and accurately is critical for timely intervention and effective management of this illness. Deep learning approaches have showed promising results in a variety of medical image processing jobs in recent years. This paper describes a novel deep learning-based approach for automatic kidney stone diagnosis utilising medical imaging data. A convolutional neural network (CNN) architecture is used in the suggested method to identify and classify kidney stones in medical photographs. A huge collection of kidney stone images is first collected and preprocessed to ensure homogeneity and improve feature extraction capabilities. To optimise its performance, the CNN model is trained on this dataset using a large number of annotated samples. The trained CNN model distinguishes kidney stone presence from healthy regions in medical pictures with good accuracy and robustness. It detects kidney stones of various sizes and shapes while overcoming hurdles given by different stone compositions and human anatomy. Furthermore, the deep learning model has fast processing speeds, making it suited for real-time clinical applications. Extensive validation and testing on an independent dataset are performed to evaluate the model's performance. The results show that the proposed deep learning method is effective in autonomous kidney stone identification, with sensitivity, specificity, and accuracy metrics comparable to or exceeding those of existing classical methods.