Home // ACHI 2018, The Eleventh International Conference on Advances in Computer-Human Interactions // View article

Detection and Classification of RBCs and WBCs in Urine Analysis with Deep Network

Authors:
Xingguo Zhang
Guoyue Chen
Kazuki Saruta
Yuki Terata

Keywords: Urinary Sediment; RBC; WBC; Faster R-CNN; Applications in Medicine

Abstract:
Urinary sediment examination is used to evaluate the possible urinary tract diseases of patients. Currently, numerous approaches are applied to automatically detect Red Blood Cells (RBCs) and White Blood Cells (WBCs) from urinary sediment images. However, it is still a challenging task due to the cellular heterogeneity. Deep learning approaches have been shown to produce encouraging results on image detection in various tasks. In this paper, we investigate issues involving Faster Regions with Convolutional Neural Network (Faster R-CNN) for the construction of an end-to-end urine analysis system. We propose an effective baseline for RBCs and WBCs detection on urinary sediment images by using a pre-train Faster R-CNN model. We evaluate our urine analysis system on a large dataset of urinary sediment images which consist of more than 6,000 annotated RBCs and WBCs images. Our results show competitive accuracy and acceptable run time. Prospectively, the proposed methods could provide support to pathology practice in terms of quantitative analysis of tissue constituents in whole-slide images, and it could potentially lead to a better understanding of urinary tract diseases.

Pages: 194 to 198

Copyright: Copyright (c) IARIA, 2018

Publication date: March 25, 2018

Published in: conference

ISSN: 2308-4138

ISBN: 978-1-61208-616-3

Location: Rome, Italy

Dates: from March 25, 2018 to March 29, 2018