1 research outputs found
CNN-Based Automatic Urinary Particles Recognition
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.Comment: The manuscript has been submitted to Journal of Medical Systems on
Jul 02. 201