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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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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

2022Journal of Radiography and Radiation SciencesJournalOpen access

10.48153/jrrs.v32i1.223080 Full text2 citations

Authors

5 authors across 5 institutions in 1 country.

Christopher C. OhagwuFirst authorCorresponding
- University of Nigeria (NG)
Kenneth K. Agwu
- Medical Laboratory Science Council of Nigeria (NG)
- Nnamdi Azikiwe University (NG)
Christian O. Onyekelu
- Benue State University (NG)
Hameed Mohammad
- Bayero University Kano (NG)
M. AbbaSenior author
- Nnamdi Azikiwe University (NG)
as printed: Mohammed Abba

Fields

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

Brain Tumor Detection and Classificationlevel 3 · 0.99
Radiomics and Machine Learning in Medical Imaginglevel 3 · 0.99
Medical Imaging and Analysislevel 3 · 0.96

Sources

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

openalexseen September 9, 2026
2022 · Journal of Radiography and Radiation Sciences · 2 citations
https://openalex.org/W3014136566

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