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 ins...

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Bibliographic Details
Main Authors: Rui, Qin, Sinuo, Liu, Toe, Teoh Teik, Brister, Brian
Format: Conference Proceeding
Language:English
Subjects:
Classification algorithms
CNN
Convolutional neural networks
image classification
Kidney
machine learning
Medical diagnostic imaging
Medical services
Python
Stochastic processes
Training
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Summary: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%.
ISSN:2831-6983
DOI:10.1109/ICAIIC57133.2023.10067085