Kidney X-ray Images Classification using Machine Learning and Deep Learning Methods

Authors : Işıl AKSAKALLI, Sibel KAÇDIOĞLU, Y Sinan HANAY

Pages : 144-151

Doi:10.17694/bajece.878116

View : 40 | Download : 11

Publication Date : 2021-04-30

Article Type : Research Paper

Abstract :Today, kidney stone detection is done manually on medical images. This process is time-consuming and subjective as it depends on the physician. This study aims to classify healthy or patient persons according to the status of kidney stones from medical images using various machine learning methods and Convolutional Neural Networks insert ignore into journalissuearticles values(CNNs);. We evaluated various machine learning methods such as Decision Trees insert ignore into journalissuearticles values(DT);, Random Forest insert ignore into journalissuearticles values(RF);, Support Vector Machines insert ignore into journalissuearticles values(SVC);, Multilayer Perceptron insert ignore into journalissuearticles values(MLP);, K-Nearest Neighbor insert ignore into journalissuearticles values(kNN);, Naive Bayes insert ignore into journalissuearticles values(BernoulliNB);, and deep neural networks using CNN. According to the experiments, the Decision Tree Classifier insert ignore into journalissuearticles values(DT); has the best classification result. This method has the highest F1 score rate with a success rate of 85.3% using the S+U sampling method. The experimental results show that the Decision Tree Classifierinsert ignore into journalissuearticles values(DT); is a feasible method for distinguishing the kidney x-ray images.
Keywords : kidney disease detection, classification, deep learning, machine learning, artificial intelligence in medicine