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

Publication2024

ADHDNet: A DNN Based Framework for Efficient ADHD Detection from fMRI Dataset

The record

Type
Publication
Date
1 January 2024
Level
National
Journal
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics
Volume
13102 LNCS
Pages
137-147
Published
1 January 2024
DOI
10.1007/978-3-031-12700-7_15
Scopus ID
85200681439
ISSN
03029743
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

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

    TargetPrimaryHigh confidenceJudged

    Deep neural network framework ADHDNet for ADHD detection from fMRI achieved 100% test accuracy, enabling computer-aided neurological disorder 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

  2. 9.5 Enhance scientific research, upgrade technological capabilities, and increase researchers and R&D spending

    TargetSecondaryHigh confidenceJudged

    Complex network analysis and deep learning methodology for brain imaging represents research innovation in neuroimaging classification.

    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

    Early ADHD detection through AI supports child health and proper clinical intervention for neurodevelopmental disorders.

    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.

Evidence

Kortex hosts no documents. Evidence is an outbound link to the authority that holds it.

No outbound evidence link is recorded for this activity. The record below states which source it came from and when it was last read.

Provenance

Which source system this record came from, how many source rows were resolved into it, and when each was last read.

Source rows resolved into this record
1

Each record carries the date Kortex last read it from its source system. That is a freshness date and not a verification: Kortex records what its sources say and when it read them, and it does not check those statements against any external authority. No claim on this site is described as verified, because none is.

Citing this record

This URL is stable. A record’s permalink is frozen when it is published and is not regenerated if its title is later corrected.

KIIT Deemed to be University. “ADHDNet: A DNN Based Framework for Efficient ADHD Detection from fMRI Dataset”. Kortex.

Explore the full record