Publication2023
Light-UNet++: A Simplified U-NET++ Architecture for Multimodal Biomedical Image Segmentation
- Good Health and Well-being
- Industry, Innovation and Infrastructure
- Empowered Indians
The record
- Type
- Publication
- Date
- 1 January 2023
- Level
- National
- Journal
- Proceedings of IEEE Inc4 2023 2023 IEEE International Conference on Contemporary Computing and Communications
- Published
- 1 January 2023
- DOI
- 10.1109/inc457730.2023.10263001
- Scopus ID
- 85174704328
- Authors
- 5
- Open access
- No
- Citations
- 5
- 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
Light-UNet++ architecture for multimodal biomedical image segmentation achieves 99.66% accuracy with 0.9069 F1 score, enabling improved early detection of medical abnormalities.
- 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
Deep learning architecture innovation for medical imaging demonstrates research contribution in computational methods and neural network optimization.
- 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 Empowered Indians
Derived through the seeded SDG-to-Viksit-Bharat crosswalk from the primary SDG 3 label. Light-UNet++ architecture for multimodal biomedical image segmentation achieves 99.66% accuracy with 0.9069 F1 score, enabling improved early detection of medical abnormalities.
- 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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- 1
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Citing this record
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KIIT Deemed to be University. “Light-UNet++: A Simplified U-NET++ Architecture for Multimodal Biomedical Image Segmentation”. Kortex.