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Clustering-based dual deep learning architecture for detecting red blood cells in malaria diagnostic smears

Abstract:

Computer-assisted algorithms have become a mainstay of biomedical applications to improve accuracy and reproducibility of repetitive tasks like manual segmentation and annotation. We propose a novel pipeline for red blood cell detection and counting in thin blood smear microscopy images, named RBCNet, using a dual deep learning architecture. RBCNet consists of a U-Net first stage for cell-cluster or superpixel segmentation, followed by a second refinement stage Faster R-CNN for detecting smal...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1109/jbhi.2020.3034863

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Name:
Intramural Research Program
Publisher:
Institute of Electrical and Electronics Engineers
Journal:
IEEE Journal of Biomedical and Health Informatics More from this journal
Volume:
25
Issue:
5
Pages:
1735-1746
Publication date:
2020-10-29
Acceptance date:
2020-09-30
DOI:
EISSN:
2168-2208
ISSN:
2168-2194
Pmid:
33119516
Language:
English
Keywords:
Pubs id:
1176488
Local pid:
pubs:1176488
Deposit date:
2021-12-08

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