Publication2022
Effect of learning parameters on the performance of U-Net Model in segmentation of Brain tumor
The record
- Type
- Publication
- Date
- 1 October 2022
- Level
- National
- Journal
- Multimedia Tools and Applications
- Volume
- 81
- Issue
- 24
- Pages
- 34717-34735
- Published
- 1 October 2022
- DOI
- 10.1007/s11042-021-11273-5
- Scopus ID
- 85113497754
- ISSN
- 13807501
- Authors
- 5
- Open access
- No
- Citations
- 41
- 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 U-Net model for automatic brain tumor segmentation from MRI enabling earlier glioma diagnosis and supporting treatment planning for cancer patients
- 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
9.5 Enhance scientific research, upgrade technological capabilities, and increase researchers and R&D spending
Optimization of neural network learning parameters for biomedical image segmentation advancing research capacity in artificial intelligence methodology
- Moderate confidence
- The alignment is plausible and defensible but rests on an assumption a reasonable reviewer could decline. Published, and surfaced in the review queue for a human to confirm or override.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Viksit Bharat @2047
vb-1.2 Health and well-being
AI-driven medical imaging technology for early cancer detection and diagnosis supporting improved health outcomes through technology-enabled healthcare
- 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. “Effect of learning parameters on the performance of U-Net Model in segmentation of Brain tumor”. Kortex.