Publication2026
FASTER YOLO: AN EFFICIENT FRAMEWORK FOR CERVICAL CANCER CELL DETECTION WITH DEFORMABLE CONVOLUTIONAL ATTENTION
The record
- Type
- Publication
- Date
- 30 April 2026
- Level
- National
- Journal
- Journal of Theoretical and Applied Information Technology
- Volume
- 104
- Issue
- 8
- Pages
- 64-75
- Published
- 30 April 2026
- Scopus ID
- 105038952838
- ISSN
- 19928645
- Authors
- 3
- 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
Faster YOLO deep learning framework achieves 87% detection accuracy for cervical cancer cell nuclei identification, enabling automated cytopathology screening.
- 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
Deformable convolutional neural network and YOLOv13 optimization for medical object detection advance oncology imaging and AI innovation.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Viksit Bharat @2047
vb-1.2 Health and well-being
Cervical cancer early detection through automated screening supports cancer prevention and women's health 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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Provenance
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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. “FASTER YOLO: AN EFFICIENT FRAMEWORK FOR CERVICAL CANCER CELL DETECTION WITH DEFORMABLE CONVOLUTIONAL ATTENTION”. Kortex.