Harnessing the Power of CNN -SVM for Kidney Disease Classification: A Comprehensive Study

This study presents an innovative classification of kidney diseases. The stated method utilizes a hybrid model that integrates CNN and Support Vector Machines. The main goal of this study is to enhance the precision and efficiency of medical image processing in the early detection and classification of kidney diseases. A large data set was used to train and test the suggested model. The test results show that the CNN-SVM hybrid model performs well, with over 97% accuracy and recall rates across all classes. This dilemma has arisen as a result of the hybrid model. Another idea that underlines this fact is the accuracy of 99% overall, which shows the model can catch situations well. The findings presented in this article imply that the hybrid method can be a useful tool for nephrologists. This method is a reliable instrument in the diagnosis and characterization of early stages. As a result, the present study has made some contribution to medical image analysis tool development and there is hope that patient renal outcomes will improve.