Published February 27, 2025 | Version v1
Dataset Open

Red Blood Cell Morphology Dataset for Image Classification

Authors/Creators

  • 1. ROR icon Université Djilali de Sidi Bel Abbès

Contributors

  • 1. ROR icon Université Djilali de Sidi Bel Abbès

Description

The dataset was compiled from multiple sources:

  1. Chula RBC-12-Dataset (Naruenatthanaset et al., 2021):  The dataset includes the following classes: Normal, Macrocyte, Microcyte, Spherocyte, Target cell, Stomatocyte, Ovalocyte, Teardrop, Burr cell, Schistocyte, uncategorized, Hypochromia, and Elliptocyte. 
  2. ThalassemiaPBS-Dataset (Tyas et al., 2022): This dataset represents nine distinct cell types. 
  3. Our samples: This supplementary collection was provided by the Anti-Cancer Centre in El-Oued, Algeria. It contains 13 blood smear images representing five different RBC disorders: Burr cells, Ovalocyte, Teardrop. Schistocyte, and Stomatocyte These samples were provided in May 2024 using an optical microscope at 1000x magnification.
We cropped each blood smear image from the Chula RBC-12 Dataset to cepture single RBCs and relevant ROI. We then categorized them according to their class using the referenced files provided by the authors.
The "Label" folder found in the dataset is structured as follows: x and y coordinates, and the RBC type represented as values from 1 to 11.
We did the same on the samples we got from the ACC. The labelling was done by specialists in the centre.

Files

Files (93.7 MB)

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Additional details

Related works

Is source of
Dataset: 10.1016/j.dib.2022.107886 (DOI)
Dataset: arXiv:2012.01321 (arXiv)

Dates

Submitted
2025-02-27

References

  • Naruenatthanaset, K., Chalidabhongse, T.H., Palasuwan, D., Anantrasirichai, N., Palasuwan, A.: Red Blood Cell Segmentation with Overlapping Cell Sepa- ration and Classification on Imbalanced Dataset. Preprint at https://arxiv.org/ abs/2012.01321 (2021)
  • Tyas, D.A., Ratnaningsih, T., Harjoko, A., Hartati, S.: Erythrocyte (red blood cell) dataset in thalassemia case. Data in Brief 41, 107886 (2022) https://doi. org/10.1016/j.dib.2022.107886