Kidney Condition Detection

This study addresses the need for early kidney disease detection by using CT imaging and advanced deep learning models to classify conditions such as cysts, stones, tumors, and normal kidneys. The approach combines transfer learning (e.g., VGG16, ResNet50), image processing techniques, and hyperparameter optimization to improve diagnostic accuracy. The model achieved up to 99.96% accuracy, demonstrating strong potential for highly reliable and automated kidney disease classification in medical imaging.