• Corpus ID: 227247725

Red Blood Cell Segmentation with Overlapping Cell Separation and Classification on Imbalanced Dataset

@article{Naruenatthanaset2020RedBC,
  title={Red Blood Cell Segmentation with Overlapping Cell Separation and Classification on Imbalanced Dataset},
  author={Korranat Naruenatthanaset and Thanarat Horprasert Chalidabhongse and Duangdao Palasuwan and Nantheera Anantrasirichai and Attakorn Palasuwan},
  journal={ArXiv},
  year={2020},
  volume={abs/2012.01321}
}
Automated red blood cell classification on blood smear images helps hematologist to analyze RBC lab results in less time and cost. Overlapping cells can cause incorrect predicted results that have to separate into multiple single RBCs before classifying. To classify multiple classes with deep learning, imbalance problems are common in medical imaging because normal samples are always higher than rare disease samples. This paper presents a new method to segment and classify red blood cells from… 
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Shape-Aware Fine-Grained Classification of Erythroid Cells

A novel shape-aware image classification network for fine-grained erythroid cell classification that can generally outperform recent state-of-the-art works on classifying the WBC.

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