Kidney Diseases Detection Based on Convolutional Neural Network

The purpose of this paper is to apply convolutional neural networks to help diagnose patients with kidney disease. Findings are divided into four types: kidney tumor, cyst, normal and stones. Currently large numbers of people engage in unhealthy lifestyles with poor diet, sedentary activity, and insufficient sleep, often resulting in kidney disease. Early detection is necessary so preventative actions can be taken to help the kidneys recover. Traditional detection is complex and imprecise, while computational diagnosis promises more rapid and accurate results. Convolutional Neural Networks (CNN), part of deep learning, are appropriate diagnostic tools already being used in medical image identification and disease classification. Here we show CNN diagnosis with ultimate training accuracies up to 98% and test accuracies up to 99%.