Deep learning-based automated classification of endometrial lesions in IVF patients using hysteroscopic images

Marah Barakat, Rasha Massoud, Marwan Alhalabi

Open source

DOI: 10.1038/s41598-026-57032-0
Published: 2026-06-06
Container: Scientific Reports
Publisher: Springer Science and Business Media LLC
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1038/s41598-026-57032-0,
  title = {Deep learning-based automated classification of endometrial lesions in IVF patients using hysteroscopic images},
  author = {Marah Barakat and Rasha Massoud and Marwan Alhalabi},
  year = {2026},
  journal = {Scientific Reports},
  doi = {10.1038/s41598-026-57032-0},
  url = {https://doi.org/10.1038/s41598-026-57032-0}
}

RIS

TY  - JOUR
TI  - Deep learning-based automated classification of endometrial lesions in IVF patients using hysteroscopic images
AU  - Marah Barakat
AU  - Rasha Massoud
AU  - Marwan Alhalabi
PY  - 2026
JO  - Scientific Reports
DO  - 10.1038/s41598-026-57032-0
UR  - https://doi.org/10.1038/s41598-026-57032-0
ER  -

APA

Barakat, M., Massoud, R., & Alhalabi, M. (2026). Deep learning-based automated classification of endometrial lesions in IVF patients using hysteroscopic images. Scientific Reports. https://doi.org/10.1038/s41598-026-57032-0

Source records

crossref · retrieved 2026-11-11T00:53:55.232Z