Publication2020
A survey on prematurity detection of diabetic retinopathy based on fundus images using deep learning techniques
The record
- Type
- Publication
- Date
- 16 October 2020
- Level
- National
- Journal
- Deep Learning Applications in Medical Imaging
- Pages
- 140-155
- Published
- 16 October 2020
- DOI
- 10.4018/978-1-7998-5071-7.ch006
- Scopus ID
- 85128311436
- Authors
- 2
- Open access
- No
- Citations
- 12
- 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 Ensure healthy lives and promote well-being for all at all ages
Deep learning algorithms for early diabetic retinopathy detection from fundus images enable timely clinical intervention and prevent vision loss in diabetic patients.
- 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
vb-1.2 Health and well-being
Computer-aided disease detection technology improves health outcomes for diabetic populations through early diagnosis and preventive vision care.
- 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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- 1
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Citing this record
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KIIT Deemed to be University. “A survey on prematurity detection of diabetic retinopathy based on fundus images using deep learning techniques”. Kortex.