Automated detection of kidney stones from CT scan images using handcrafted texture features and ensemble machine lLearning

Background

Nephrolithiasis (stone in kidney) is the most frequently encountered problem in urology, which affects around 10 percent of people during their lifetime. Non-Contrast Computed Tomography (CT) is the gold standard for diagnosis; however, its manual reading is very time-consuming, subjective and dependent on different readers. The aim of this study was to design a fully automated machine learning pipeline for classifying CT images into two classes, i.e., Normal and Stone.