# CellSegUNet: an improved deep segmentation model for the cell segmentation based on UNet++ and residual UNet models

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/cellsegunet-an-improved-deep-segmentation-model-for-the-cell-segmentation-based/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Sedat Metlek |
| Citations | 39 |
| DOI | 10.1007/s00521-023-09374-3 |
| Fields | Biochemistry, Genetics and Molecular Biology,Computer Science |
| Open Access | true |
| OA Status | hybrid |
| OA URL | https://link.springer.com/content/pdf/10.1007/s00521-023-09374-3.pdf |
| OpenAlex ID | https://openalex.org/W4390837937 |
| Type | article |
| Year | 2024 |

## Paper authors

- [Sedat Metlek](https://scholariq.org/researchers/sedat-metlek/)

## Paper primary topic

- [Digital Imaging for Blood Diseases](https://scholariq.org/topics/digital-imaging-for-blood-diseases/)

## Paper topics

- [Digital Imaging for Blood Diseases](https://scholariq.org/topics/digital-imaging-for-blood-diseases/)
- [AI in cancer detection](https://scholariq.org/topics/ai-in-cancer-detection/)
- [Cell Image Analysis Techniques](https://scholariq.org/topics/cell-image-analysis-techniques/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.