Publication2024
A Transfer Learning and Feature Ranking-based Feature Extraction Approach for Enhancing Skin Lesion Classification
The record
- Type
- Publication
- Date
- 1 January 2024
- Level
- National
- Journal
- Esic 2024 4th International Conference on Emerging Systems and Intelligent Computing Proceedings
- Pages
- 607-613
- Published
- 1 January 2024
- DOI
- 10.1109/esic60604.2024.10481593
- Scopus ID
- 85190950793
- Authors
- 4
- Open access
- No
- Citations
- 2
- 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 Ensure healthy lives and promote well-being for all at all ages
Transfer learning and feature ranking methods enhance skin lesion classification accuracy using deep neural networks (VGG16, ResNet, EfficientNet), supporting melanoma and non-melanoma cancer diagnosis.
- 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
Research demonstrates advanced AI methodology application for medical image analysis and diagnostic algorithm development in computational healthcare.
- 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
Improved skin cancer detection through machine learning advances health outcomes for patients with dermatologic malignancies and supports early diagnostic intervention.
- 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. “A Transfer Learning and Feature Ranking-based Feature Extraction Approach for Enhancing Skin Lesion Classification”. Kortex.