Authors : Hervé Kinkete Mfumabi; Matangila Gradi; Nsiala Ndona Clémence; Kavugho Kahasa Thérèse; Kalema Manzambie Jordanie; Kema Mulumba Joël; Madio Motemona Godard; Ilolo Nana

Volume/Issue : Volume 11 - 2026, Issue 8 - August

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Google Scholar : https://tinyurl.com/29ttkvy3

DOI : https://doi.org/10.38124/ijisrt/26aug907

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

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Abstract : High aggregate performance does not guarantee reliable individual predictions or clinical transportability. This study designed, implemented, and internally evaluated NEUROCALIB, a selective bimodular research framework comprising independently assessed classification and segmentation pipelines, and audited their integration into the NeuroVision web prototype. EfficientNetB0 was evaluated on 6,683 public brain MRI images across four classes; MobileNetV2–U-Net was evaluated on 4,048 image-mask pairs after removal of 189 exact duplicates. Validation data were used for model selection, temperature scaling, and abstention thresholds before the test sets were opened.

Keywords : Brain MRI; Brain Tumor; Calibration; EfficientNetB0; Explainable AI; FastAPI; Selective Prediction; Segmentation; Uncertainty.

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