MRI-Based Brain Tumor Detection for the African Context

Jideofor J. Umeadi
Proceedings of IndabaX Nigeria 2026: Building Scalable AI That Works: From Research to Deployment in Resource-Constrained Environments, PMLR 319:179-190, 2026.

Abstract

This study evaluates lightweight transfer learning for binary brain tumor detection under resource constraints in sub-Saharan Africa, with a focus on generalisation across domain shift from Western-heavy training data to actual Nigerian clinical scans. A frozen MobileNetV2 backbone trained on a skull-stripped Kaggle MRI dataset and evaluated on the BraTS-Africa cohort—scans from six Nigerian diagnostic centres—showed that skull-stripping preprocessing improved mean external sensitivity from $52.67% \pm 24.04%$ to $85.67% \pm 13.58%$ across three random seeds. These results demonstrate that targeted domain alignment through simple preprocessing is a viable approach to closing the generalisation gap in low-resource settings.

Cite this Paper

BibTeX
@InProceedings{pmlr-v319-umeadi26a, title = {{MRI}-Based Brain Tumor Detection for the {African} Context}, author = {Umeadi, Jideofor J.}, booktitle = {Proceedings of IndabaX Nigeria 2026: Building Scalable AI That Works: From Research to Deployment in Resource-Constrained Environments}, pages = {179--190}, year = {2026}, editor = {Folorunso, Sakinat and Ogundokun, Roseline and Oladipo, Francisca}, volume = {319}, series = {Proceedings of Machine Learning Research}, month = {11--14 May}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v319/main/assets/umeadi26a/umeadi26a.pdf}, url = {https://proceedings.mlr.press/v319/umeadi26a.html}, abstract = {This study evaluates lightweight transfer learning for binary brain tumor detection under resource constraints in sub-Saharan Africa, with a focus on generalisation across domain shift from Western-heavy training data to actual Nigerian clinical scans. A frozen MobileNetV2 backbone trained on a skull-stripped Kaggle MRI dataset and evaluated on the BraTS-Africa cohort—scans from six Nigerian diagnostic centres—showed that skull-stripping preprocessing improved mean external sensitivity from $52.67% \pm 24.04%$ to $85.67% \pm 13.58%$ across three random seeds. These results demonstrate that targeted domain alignment through simple preprocessing is a viable approach to closing the generalisation gap in low-resource settings.} }
Endnote
%0 Conference Paper %T MRI-Based Brain Tumor Detection for the African Context %A Jideofor J. Umeadi %B Proceedings of IndabaX Nigeria 2026: Building Scalable AI That Works: From Research to Deployment in Resource-Constrained Environments %C Proceedings of Machine Learning Research %D 2026 %E Sakinat Folorunso %E Roseline Ogundokun %E Francisca Oladipo %F pmlr-v319-umeadi26a %I PMLR %P 179--190 %U https://proceedings.mlr.press/v319/umeadi26a.html %V 319 %X This study evaluates lightweight transfer learning for binary brain tumor detection under resource constraints in sub-Saharan Africa, with a focus on generalisation across domain shift from Western-heavy training data to actual Nigerian clinical scans. A frozen MobileNetV2 backbone trained on a skull-stripped Kaggle MRI dataset and evaluated on the BraTS-Africa cohort—scans from six Nigerian diagnostic centres—showed that skull-stripping preprocessing improved mean external sensitivity from $52.67% \pm 24.04%$ to $85.67% \pm 13.58%$ across three random seeds. These results demonstrate that targeted domain alignment through simple preprocessing is a viable approach to closing the generalisation gap in low-resource settings.
APA
Umeadi, J.J.. (2026). MRI-Based Brain Tumor Detection for the African Context. Proceedings of IndabaX Nigeria 2026: Building Scalable AI That Works: From Research to Deployment in Resource-Constrained Environments, in Proceedings of Machine Learning Research 319:179-190 Available from https://proceedings.mlr.press/v319/umeadi26a.html.

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