Contour‐Detected Normalized Residual Model for Kidney Stone Classification

Kidney stone classification is a critical yet complex task in medical imaging, traditionally performed using computed tomography (CT) and ultrasound scans. Manual interpretation of these images is time‐consuming and prone to variability, highlighting the need for automated diagnostic solutions. This study proposes Contour‐Detected Normalized Residual VGG19 (CDR‐VGG19), a deep learning model inspired by VGG19 and enhanced with residual connections to improve classification accuracy. The model leverages contour detection for unsupervised feature extraction, followed by supervised learning using a hybrid of VGG19 and ResNet architectures. Using the Kidney Stone KAGGLE dataset of 2602 images, the model applies data augmentation, preprocessing, and feature filtering. Images are split into training, validation, and testing sets (80:10:10), and multiple CNNs are evaluated. Results show that the proposed CDR‐VGG19 achieves a high classification accuracy of 99.61%, demonstrating its effectiveness in detecting kidney stones from contour‐enhanced images.