Kidney X-ray Images Classification using Machine Learning and Deep Learning Methods
Işıl Karabey Aksakalli, Sibel Kaçdioglu, Y. Sinan Hanay
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 (CNNs). We evaluated various machine learning methods such as Decision Trees (DT), Random Forest (RF), Support Vector Machines (SVC), Multilayer Perceptron (MLP), K-Nearest Neighbor (kNN), Naive Bayes (BernoulliNB), and deep neural networks using CNN. According to the experiments, the Decision Tree Classifier (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 Classifier(DT) is a feasible method for distinguishing the kidney x-ray images.