Kidney Stone Size Estimation Using VGG16 Architecture

Kidney stone is crucial medical disease that require prompt identification and accurate assessment for effective treatment. Their development is greatly influenced by environmental variables, especially climate, with extreme heat, high humidity, and increased sweating increasing the risk. A deep learning-based technology is used in the methodology to increase learning efficiency and automate kidney stone identification using Computed Tomography(CT) scans. The methodology incorporates dropout layers, the Adam optimizer, data augmentation, and the VGG16 model. The modular approach using OpenCV for size estimation and stone feature analysis, offers a strong foundation for precise measurements. Even the smallest stones can be approximately identified by CT scans, which is well known for its great sensitivity and specificity. Accurate diagnosis and well-informed treatment planning are made possible by insights from axial and coronal view of kidney evaluations. The procedure is enhanced by the Kidney Stone Size Prediction (KSSP) algorithm, which measures stone sizes precisely, with testing and validation accuracy of 97.53% and 97.20%, respectively, using VGG16 model and a precision of 97.68 on the training dataset. The inclusive method facilitates better patient outcomes in kidney stone management, speeds up clinical work flows, and increases diagnostic accuracy. The model’s accuracy increases with the number of epochs it experiences.