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

Publication2025

Brain Tumor Classification Using CNN Based Pre-trained Model Architecture Leveraging Image Augmentation Techniques

The record

Type
Publication
Date
1 January 2025
Level
National
Journal
Learning and Analytics in Intelligent Systems
Volume
45
Pages
121-129
Published
1 January 2025
DOI
10.1007/978-3-031-82706-8_13
Scopus ID
105000967736
ISSN
26623447
Authors
4
Open access
No
Citations
0
International co-authorship
No
Co-author countries
IN

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

    Pre-trained CNN models with image augmentation achieve 99.12% accuracy for early brain tumor classification from MRI, enabling timely cancer diagnosis and treatment.

    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-1.2 Health and well-being

    Sub-themePrimaryHigh confidenceJudged

    AI-enabled early detection of brain tumors directly improves health outcomes and well-being through enhanced diagnostic capability.

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

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KIIT Deemed to be University. “Brain Tumor Classification Using CNN Based Pre-trained Model Architecture Leveraging Image Augmentation Techniques”. Kortex.

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