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by IQAC KIITKIIT Deemed to be University — Internal Quality Assurance Cell

Publication2025

ArsenicSkinNet: A Deep Learning Approach for Arsenicosis Skin Lesion Classification

The record

Type
Publication
Date
1 January 2025
Level
International — held abroad
Journal
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics
Volume
15431 LNAI
Pages
1-14
Published
1 January 2025
DOI
10.1007/978-981-96-0692-4_1
Scopus ID
86000464050
ISSN
03029743
Authors
7
Open access
No
Citations
1
International co-authorship
Yes
Co-author countries
IE, IN, US, ZA

Classification

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UN Sustainable Development Goals

  1. 3.4 Reduce premature mortality from non-communicable diseases and promote mental health and well-being

    TargetPrimaryHigh confidenceJudged

    ArsenicSkinNet deep learning model adapted from EfficientNet achieves 99.61% accuracy for automatic detection and classification of arsenicosis skin lesions.

    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

  2. 17.6 Enhance North-South, South-South and triangular cooperation on science, technology and innovation, and knowledge sharing

    TargetSecondaryHigh confidenceRule-derivedrule sdg17-structural-v1

    Structural signal: co-authorship outside India.

    Rule-derived
    Emitted by a deterministic rule from structural signals rather than judged from the record’s text. The rule carries a version, so rule-derived and judged labels stay separable.
    Produced by
    sdg17-structural-v1, under kortex-publication-v1

Viksit Bharat @2047

  1. vb-1.2 Health and well-being

    Sub-themePrimaryHigh confidenceJudged

    Environmental chemical disease diagnosis technology supports early detection and management for affected populations.

    Produced by
    claude-haiku-4-5-20251001, under kortex-publication-v1

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KIIT Deemed to be University. “ArsenicSkinNet: A Deep Learning Approach for Arsenicosis Skin Lesion Classification”. Kortex.

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