Publication2027
Attention-ResUNet for Automated Fetal Head Segmentation
The record
- Type
- Publication
- Date
- 1 January 2027
- Level
- National
- Journal
- Communications in Computer and Information Science
- Volume
- 2921 CCIS
- Pages
- 533-550
- Published
- 1 January 2027
- DOI
- 10.1007/978-3-032-27117-4_30
- Scopus ID
- 105043675366
- ISSN
- 18650929
- Authors
- 2
- Open access
- No
- Citations
- 0
- International co-authorship
- No
- Co-author countries
- IN
Classification
Every label below carries the reasoning it was assigned on, the confidence band it was assigned in, and what produced it. The rubric is published.
UN Sustainable Development Goals
3.4 Reduce premature mortality from non-communicable diseases and promote mental health and well-being
Deep learning architecture with attention mechanisms achieves 99.30% accuracy for automated fetal head segmentation, enabling accurate prenatal biometric measurement
- High confidence
- The subject matter is a strong and direct inference from the record type, the funding or organising body, or the programme context, even where the title alone is thin. Example: an ICMR-funded project against SDG 3, on the strength of the agency.
- Judged
- Judged against the published classification rubric, under the rubric and prompt versions recorded with the run that produced it.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Viksit Bharat @2047
vb-1.2 Health and well-being
Automated ultrasound image analysis improves prenatal monitoring and early detection of fetal abnormalities for improved birth outcomes
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Each label above carries the instrument and the version that produced it. How each classification is reached.
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- Source rows resolved into this record
- 1
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Citing this record
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KIIT Deemed to be University. “Attention-ResUNet for Automated Fetal Head Segmentation”. Kortex.