  Urine sediment examination (USE) is one of the main tests used in the
evaluation of diseases such as kidney, urinary, metabolic, and diabetes and to
determine the density and number of various cells in the urine. USE's manual
microscopy is a labor-intensive and time-consuming, imprecise, subjective
process. Recently, automatic analysis of urine sediment has become inevitable
in the medical field. In this study, we propose a dataset that can be used by
artificial intelligence techniques to automatically identify particles in urine
sediment images. The data set consists of 8509 particle images obtained by
examining the particles in the urine sediment obtained from 409 patients from
the Biochemistry Clinics of Elazig Fethi Sekin Central Hospital. Particle
images are collected in 8 classes in total and these are Erythrocyte,
Leukocyte, Epithelial, Bacteria, Yeast, Cylinders, Crystals, and others (sperm,
etc.).
