Publication2025
An Optimized and Robust Mobile Application Enabled Deep Learning Framework for Laryngeal Cancer Detection Using Voice and Image Data Samples
The record
- Type
- Publication
- Date
- 1 January 2025
- Level
- National
- Journal
- Lecture Notes in Networks and Systems
- Volume
- 1291 LNNS
- Pages
- 411-422
- Published
- 1 January 2025
- DOI
- 10.1007/978-981-96-3244-2_32
- Scopus ID
- 105008998489
- ISSN
- 23673370
- Authors
- 3
- Open access
- No
- Citations
- 30
- 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
Deep learning framework for laryngeal cancer detection from voice and image data directly addresses early cancer 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
9.5 Enhance scientific research, upgrade technological capabilities, and increase researchers and R&D spending
Mobile-enabled deep learning for medical diagnosis represents research innovation in healthcare AI.
- Moderate confidence
- The alignment is plausible and defensible but rests on an assumption a reasonable reviewer could decline. Published, and surfaced in the review queue for a human to confirm or override.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Viksit Bharat @2047
vb-1.2 Health and well-being
Cancer detection technology supports early diagnosis and improved patient health outcomes.
- 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.
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- Source rows resolved into this record
- 1
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Citing this record
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KIIT Deemed to be University. “An Optimized and Robust Mobile Application Enabled Deep Learning Framework for Laryngeal Cancer Detection Using Voice and Image Data Samples”. Kortex.