# CNN-Based Automatic Urinary Particles Recognition

arXiv: https://arxiv.org/abs/1803.02699 · HTML: https://mlsanity.com/paper/1803.02699/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Mar 2018.

## Built on by (most cited first, 3 of 3)

- [Urine Microscopic Image Dataset](https://mlsanity.com/paper/2111.10374/) (2021)
- [GAN-GA: A Generative Model based on Genetic Algorithm for Medical Image Generation](https://mlsanity.com/paper/2401.00314/) (2023)
- [Video to All-in-focus Image Reconstruction Algorithm for Automated Microscopic Urinalysis](https://mlsanity.com/paper/2607.13601/) (2026)

## Abstract (arXiv)

The urine sediment analysis of particles in microscopic images can assist physicians in evaluating patients with renal and urinary tract diseases. Manual urine sediment examination is labor-intensive, subjective and time-consuming, and the traditional automatic algorithms often extract the hand-crafted features for recognition. Instead of using the hand-crafted features, in this paper, we exploit CNN to learn features in an end-to-end manner to recognize the urine particles. We treat the urine particles recognition as object detection and exploit two state-of-the-art CNN-based object detection methods, Faster R-CNN and SSD, as well as their variants for urine particles recognition. We further investigate different factors involving these CNN-based object detection methods for urine particles recognition. We comprehensively evaluate these methods on a dataset consisting of 5,376 annotated images corresponding to 7 categories of urine particles, i.e., erythrocyte, leukocyte, epithelial cell, crystal, cast, mycete, epithelial nuclei, and obtain a best mAP (mean average precision) of 84.1% while taking only 72 ms per image on a NVIDIA Titan X GPU.