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

Publication2020

Gastrointestinal diseases segmentation and classification based on duo-deep architectures

The record

Type
Publication
Date
1 March 2020
Level
National
Journal
Pattern Recognition Letters
Volume
131
Pages
193-204
Published
1 March 2020
DOI
10.1016/j.patrec.2019.12.024
Scopus ID
85078063284
ISSN
01678655
Authors
7
Open access
No
Citations
159
International co-authorship
No
Co-author countries
IN

Classification

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UN Sustainable Development Goals

  1. 3 Ensure healthy lives and promote well-being for all at all ages

    GoalPrimaryHigh confidenceJudged

    Deep learning method using Mask RCNN and ResNet101 achieves 99.13% accuracy for gastrointestinal disease classification from wireless capsule endoscopy, enabling automated 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

Viksit Bharat @2047

  1. vb-1 Empowered Indians

    ThemePrimaryHigh confidenceJudged

    Derived through the seeded SDG-to-Viksit-Bharat crosswalk from the primary SDG 3 label. Deep learning method using Mask RCNN and ResNet101 achieves 99.13% accuracy for gastrointestinal disease classification from wireless capsule endoscopy, enabling automated diagnosis.

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

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KIIT Deemed to be University. “Gastrointestinal diseases segmentation and classification based on duo-deep architectures”. Kortex.

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Gastrointestinal diseases segmentation and classification based on duo-deep architectures · Kortex