Fast and accurate automated recognition of the dominant cells from fecal images based on Faster R-CNN
Abstract Fecal samples can easily be collected and are representative of a person’s current health state; therefore, the demand for routine fecal examination has increased sharply. However, manual operation may pollute the samples, and low efficiency limits the general examination speed; therefore,...
Enregistré dans:
Auteurs principaux: | Jing Zhang, Xiangzhou Wang, Guangming Ni, Juanxiu Liu, Ruqian Hao, Lin Liu, Yong Liu, Xiaohui Du, Fan Xu |
---|---|
Format: | article |
Langue: | EN |
Publié: |
Nature Portfolio
2021
|
Sujets: | |
Accès en ligne: | https://doaj.org/article/95c125c38d2d43c6b0dde166b82e2915 |
Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
-
GMANet: Gradient Mask Attention Network for Finding Clearest Human Fecal Microscopic Image in Autofocus Process
par: Xiangzhou Wang, et autres
Publié: (2021) -
Short Circuit Recognition for Metal Electrorefining Using an Improved Faster R-CNN With Synthetic Infrared Images
par: Xin Li, et autres
Publié: (2021) -
Klasifikasi Pola Kain Tenun Melayu Menggunakan Faster R-CNN
par: Yoze Rizki, et autres
Publié: (2021) -
Pulmonary Nodule Detection Based on Faster R-CNN With Adaptive Anchor Box
par: Chi Cuong Nguyen, et autres
Publié: (2021) -
SAR Target Detection Based on Domain Adaptive Faster R-CNN with Small Training Data Size
par: Yuchen Guo, et autres
Publié: (2021)