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

Işıl AKSAKALLI,Sibel KAÇDIOĞLU,Y. Sinan HANAY

doi:10.17694/bajece.878116

Işıl AKSAKALLI, Sibel KAÇDIOĞLU + Show 1 more

Open Access

https://doi.org/10.17694/bajece.878116

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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.

Highlights

KIDNEY STONE disease occurs due to the accumulation of salt and mineral crystals excreted in the urine and turning into stones
After the resampling process is completed, 80% of the dataset (182 image values) is trained, and precision, recall, and F1 score performance metrics are evaluated with the StratifiedKFold cross-validation method
The classification performance of the methods depends on the number of correctly detected classes (TP-Correct Positive), the number of healthy people identified as patients (FP-False Positive), and the number of patients identified as healthy (FN-False Negative)

Summary

Introduction

KIDNEY STONE disease occurs due to the accumulation of salt and mineral crystals excreted in the urine and turning into stones. Kidney stones have been affecting people for centuries. It is one of the most common diseases of the kidneys and urinary tract. It affects approximately 1-15% of the world population, and its prevalence is increasing with each passing year [1]. The prevalence of kidney stones was 1– 5% in Asia, 5–9% in Europe, and 7–15% in North America [1]. Kidney stone formation can occur at any age, gender, and https://orcid.org/0000-0002-4156-9098

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