Paper Info
Title
An End-to-End System for Automatic Urinary Particle Recognition with Convolutional Neural Network.
Abstract
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 propose to exploit convolutional neural network (CNN) to learn features in an end-to-end manner to recognize the urinary particle. We treat the urinary particle recognition as object detection and exploit two state-of-the-art CNN-based object detection methods, Faster R-CNN and single shot multibox detector (SSD), along with their variants for urinary particle recognition. We further investigate different factors involving these CNN-based methods to improve the performance of urinary particle recognition. We comprehensively evaluate these methods on a dataset consisting of 5,376 annotated images corresponding to 7 categories of urinary particle, i.e., erythrocyte, leukocyte, epithelial cell, crystal, cast, mycete, epithelial nuclei, and obtain a best mean average precision (mAP) of 84.1% while taking only 72 ms per image on a NVIDIA Titan X GPU.
Year
DOI
Venue
2018
10.1007/s10916-018-1014-6
J. Medical Systems
Keywords
Field
DocType
CNN,Faster R-CNN,SSD,Urinary particle recognition
Object detection,Data mining,End to end system,Pattern recognition,Convolutional neural network,Urinary Tract Diseases,Artificial intelligence,Urine sediment,Detector,Medicine,Particle,Urinary system
Journal
Volume
Issue
ISSN
42
9
0148-5598
Citations 
PageRank 
References 
1
0.63
21
Authors
4
Name
Order
Citations
PageRank
Yixiong Liang153.41
Rui Kang244.76
Chunyan Lian310.63
Yuan Mao411.64