Abstract: This paper presents a Convolutional Neural Network (CNN) based approach for kidney stone detection using medical imaging data. Kidney stones, or renal calculi, are solid deposits formed in the kidneys, causing intense pain and complications. Early detection is crucial for timely intervention and treatment. The proposed CNN model utilizes deep learning techniques to analyze CT scans or ultrasound images for the presence of kidney stones. The model's architecture includes convolutional layers for feature extraction, followed by pooling layers for spatial dimension reduction, and fully connected layers for classification. Experimental results demonstrate the effectiveness of the CNN in accurately detecting kidney stones, with high sensitivity and specificity. The proposed approach offers a promising solution for automated kidney stone detection, aiding healthcare professionals in efficient diagnosis and patient care.

| DOI: 10.17148/IARJSET.2024.11498