DeepKidney: Multiclass Classification of Kidney Stones, Cysts, Tumors, and Normal Cases Using Convolutional Neural Networks

Accurately classifying kidney diseases, such as stones, cysts, tumors, and normal cases, is vital in effective diagnosis and treatment planning. A method for multi class classification of kidney conditions using Convolutional Neural Networks (CNNs) is proposed in this paper. The main aim is to develop a system, termed DeepKidney, that can accurately classify medical imaging data automatically. This study focuses on the multiclass classification of kidney stones, cysts, tumors, and normal cases using CNNs. We propose a novel approach called DeepKidney, which utilizes CNNs to analyze medical imaging data and extract relevant features for classification. A large and diverse dataset comprising kidney images representing different conditions is used for training the CNN model. DeepKidney aims to achieve high classification accuracy and improve diagnostic outcomes. This approach provides a comprehensive solution for distinguishing between different kidney conditions, aiding in precise diagnoses. DeepKidney's implementation has the potential to increase kidney disease classification's precision and effectiveness, resulting in better patient outcomes and less work for medical professionals.