Intelligent Systems for Kidney Stone Prediction: Harnessing Deep Learning Techniques

For precise diagnosis and treatment planning, kidney CT image classification into groups like Normal, Cyst, Tumor, and Stone is essential. A convolutional neural network (CNN) method for the automatic classification of kidney CT scans is presented in this research. To improve the training process, preprocessed and enhanced photos are used from a Google Drive collection. Several convolutional layers with batch normalization, max pooling, and dropout for regularization are included in the suggested model. The Image DataGenerator is used to enrich data in order to reduce overfitting and enhance generalization. Through the use of callbacks for early halting and learning rate adjustment during training, the model achieved a promising level of accuracy on the test set. The effectiveness of deep learning approaches in medical picture classification is demonstrated in this work, which may help radiologists with diagnostic duties.