Publication2025
Hybrid UNet-LSTM Deep Network for Intracerebral Hemorrhage Detection on Brain CT
The record
- Type
- Publication
- Date
- 1 January 2025
- Level
- National
- Journal
- 2025 10th International Conference on Research in Intelligent Computing in Engineering Rice 2025
- Published
- 1 January 2025
- DOI
- 10.1109/rice67503.2025.11439922
- Scopus ID
- 105037455815
- Authors
- 4
- 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 Ensure healthy lives and promote well-being for all at all ages
Hybrid UNet-LSTM deep learning model achieves 97.91% accuracy for intracerebral hemorrhage detection on brain CT scans, enabling rapid early-stage stroke 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
Viksit Bharat @2047
vb-3.2 Digital technology and artificial intelligence
AI-driven medical imaging analysis for disease detection represents advanced digital technology application in healthcare.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
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- 1
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Citing this record
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KIIT Deemed to be University. “Hybrid UNet-LSTM Deep Network for Intracerebral Hemorrhage Detection on Brain CT”. Kortex.