Publication2026
Enhancing Diabetic Retinopathy Diagnosis Using Deep Learning-Based Approach for Accurate Detection
- Good Health and Well-being
- Industry, Innovation and Infrastructure
- Empowered Indians
The record
- Type
- Publication
- Date
- 1 January 2026
- Level
- National
- Journal
- Proceedings International Conference on Computational Intelligence and Networks
- Published
- 1 January 2026
- DOI
- 10.1109/cine68769.2026.11502856
- Scopus ID
- 105041133156
- ISSN
- 23755822
- Authors
- 6
- Open access
- No
- Citations
- 0
- 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 using DenseNet and VGG architectures achieves high accuracy in automated diabetic retinopathy detection from retinal images.
- 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
Comparative evaluation of convolutional neural network architectures for disease diagnosis classification represents AI innovation in healthcare.
- 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 Empowered Indians
Derived through the seeded SDG-to-Viksit-Bharat crosswalk from the primary SDG 3 label. Deep learning using DenseNet and VGG architectures achieves high accuracy in automated diabetic retinopathy detection from retinal images.
- 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. “Enhancing Diabetic Retinopathy Diagnosis Using Deep Learning-Based Approach for Accurate Detection”. Kortex.