Publication2024
Multiclass skin lesion classification using image augmentation technique and transfer learning models
- Good Health and Well-being
- Industry, Innovation and Infrastructure
- Empowered Indians
The record
- Type
- Publication
- Date
- 16 April 2024
- Level
- National
- Journal
- International Journal of Intelligent Unmanned Systems
- Volume
- 12
- Issue
- 2
- Pages
- 220-228
- Published
- 16 April 2024
- DOI
- 10.1108/ijius-02-2021-0010
- Scopus ID
- 85107761517
- ISSN
- 20496427
- Authors
- 3
- Open access
- No
- Citations
- 23
- 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
Transfer learning of pre-trained convolutional neural networks achieves 83.69% accuracy for multiclass skin lesion classification, enabling automated early detection of melanoma and non-melanoma skin cancers for timely clinical intervention.
- 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
Application of VGG, ResNet, Xception, and MobileNet transfer learning models to dermoscopic image analysis demonstrates innovation in deep learning methodologies for medical image classification and automated diagnosis systems.
- 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. Transfer learning of pre-trained convolutional neural networks achieves 83.69% accuracy for multiclass skin lesion classification, enabling automated early detection of melanoma and non-melanoma skin cancers for timely clinical intervention.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
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- 1
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Citing this record
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KIIT Deemed to be University. “Multiclass skin lesion classification using image augmentation technique and transfer learning models”. Kortex.