Publication2020
Automated segmentation and classification of brain stroke using expectation-maximization and random forest classifier
The record
- Type
- Publication
- Date
- 1 January 2020
- Level
- National
- Journal
- Biocybernetics and Biomedical Engineering
- Volume
- 40
- Issue
- 1
- Pages
- 277-289
- Published
- 1 January 2020
- DOI
- 10.1016/j.bbe.2019.04.004
- Scopus ID
- 85076708081
- ISSN
- 02085216
- Authors
- 3
- Open access
- No
- Citations
- 189
- 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
Expectation-maximization segmentation and random forest classification automates ischemic stroke detection from MRI, achieving 93.4% accuracy for OCSP classification.
- 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
AI-enabled stroke diagnosis and emergency medical decision support.
- 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. “Automated segmentation and classification of brain stroke using expectation-maximization and random forest classifier”. Kortex.