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Sankore

Named after the University of Sankore in Timbuktu — one of the world’s oldest centres of learning. Every figure here is computed from work-level records in our own graph, never copied from a source’s summary count.

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Coverage is partial and says so. A country appears with its research output only once its works have been swept; until then the portal shows whatever the graph happens to hold for it and labels it as exactly that.

CHARACTERIZATION AND CLASSIFICATION OF BRAIN TISSUE AND STROKE LESIONS IN NON-CONTRAST COMPUTED TOMOGRAPHY IMAGES OF STROKE PATIENTS USING STATISTICAL TEXTURE DESCRIPTORS AND ARTIFICIAL NEURAL NETWORK

2025JRRSJournalOpen access

10.82547/jrrs.vol32no1.69 Full text0 citations

Authors

5 authors across 3 institutions in 1 country.

Ohagwu, ph. DFirst author
Agwu, Ph. D
- University of Nigeria (NG)
Onyekelu, B Sc
Mohammad, FMCR
- Benue State University (NG)
Abba, M ScSenior author
- Bayero University Kano (NG)

Fields

Assignment is probabilistic — a work belongs to several fields in proportions.

Brain Tumor Detection and Classificationlevel 3 · 0.59
Acute Ischemic Stroke Managementlevel 3 · 0.04
Medical Imaging and Analysislevel 3 · 0.03

Sources

Several sources describing one work is the point — it means the record rests on more than one authority.

openalexseen September 9, 2026
2025 · JRRS · 0 citations
https://openalex.org/W7126186557

CHARACTERIZATION AND CLASSIFICATION OF BRAIN TISSUE AND STROKE LESIONS IN NON-CONTRAST COMPUTED TOMOGRAPHY IMAGES OF STROKE PATIENTS USING STATISTICAL TEXTURE DESCRIPTORS AND ARTIFICIAL NEURAL NETWORK · Sankore