Kidney Stone Detection Using Deep Learning and Transfer Learning
Sesha Vidhya S, D Vishmitha, K Yoshika, P. Sivalakshmi, V Likitha Chowdary, K G Shanthi, MuttumVenkata Yamini
Abstract
Researchers are now interested in the invention and improvement of diagnostic instruments for medical diagnosis. One of the technologies utilized in medical inspection and diagnosis is deep learning. The utilization of various data mining algorithms on kidney patient data sets is investigated in this study. The purpose of this research is to employ data mining classifiers to predict kidney failure. Back Propagation Convolutional Neural Network is one of the methods utilized for this diagnostic system. The outcomes of the tests show that the Convolutional neural network (CNN) algorithm outperforms than other classification systems. An automated kidney stone classification is implemented using a Convolutional Neural Network (CNN) image and data processing techniques. It is impossible to produce results for large datasets using human inspection and operators. As a result, this research work utilizes the Convolutional Neural Network (CNN) and the ALEXNET algorithm to overcome the challenge.