Clustering-Based Dual Deep Learning Architecture for Detecting Red Blood Cells in Malaria Diagnostic Smears

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 RBCNe...

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Bibliographic Details
Main Authors:	Antani, S. (Author), Jaeger, S. (Author), Kassim, Y.M (Author), Maude, R.J (Author), Palaniappan, K. (Author), Palaniappan, N. (Author), Poostchi, M. (Author), Yang, F. (Author)
Format:	Article
Language:	English
Published:	Institute of Electrical and Electronics Engineers Inc. 2021
Subjects:	accuracy anemia Article artificial neural network Bioinformatics Biomedical applications Blood blood smear cancer diagnosis cell separation Cells cerebral malaria cluster analysis Cluster Analysis Cluster computing Connected component connected components Cytology deep learning Deep learning Deep Learning Diagnosis Diseases entropy erythrocyte Erythrocytes faster R-CNN fever headache histogram human Humans image processing Image Processing, Computer-Assisted information processing learning algorithm Learning architectures Learning systems leukocyte machine learning malaria Malaria Malaria diagnosis Manual segmentation Medical applications memory microscopy Morphological structures nerve cell network Object detection Pipelines Plasmodium falciparum Red blood cells (RBCs) Reproducibilities reproducibility Reproducibility of Results seizure semantic segmentation superpixel Superpixel segmentations training U-Net visual acuity white blood cells (WBCs)
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Clustering-Based Dual Deep Learning Architecture for Detecting Red Blood Cells in Malaria Diagnostic Smears

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