  Deep learning has become an extremely powerful tool for complex tasks such as
image classification and segmentation. The medical industry often lacks
high-quality, balanced datasets, which can be a challenge for deep learning
algorithms that need sufficiently large amounts of data to train and increase
their performance. This is especially important in the context of kidney issues
such as for stones, cysts and tumors. We used deep learning models for this
study to classify or detect several types of kidney diseases. We use different
classification models, such as VGG-19, (CNNs) Convolutional Neural Networks,
ResNet-101, VGG-16, ResNet-50, and DenseNet-169, which can be enhanced through
techniques such as classification, segmentation, and transfer learning. These
algorithms can help improve model accuracy by allowing them to learn from
multiple datasets. This technique has the potential to revolutionize the
diagnosis and treatment of kidney problems as it enables more accurate and
effective classification of CT-scan images. This may ultimately lead to better
patient outcomes and improved overall health outcomes.
