Hybrid Ensemble Architecture for Brain Tumor Segmentation Using EfficientNetB4-MobileNetV3 with Multi-Path Decoders

Suhaila Abuowaida, Yazan Alnsour, Zaher Salah, Raed Alazaidah, Mohammad Subhi Al-Batah, Mowafaq Salem Alzboon, Nawaf Alshdaifat and Bashar Al-haj Moh’d

Data and Metadata, 2025, vol. 4, 374

Abstract: Brain tumor segmentation based on multi-modal magnetic resonance imaging is a challenging medical problem due to tumors heterogeneity, irregular boundaries, and inconsistent appearances. For this purpose, we propose a hybrid primal and dual ensemble architecture leveraging EfficientNetB4 and MobileNetV3 through a cross-network novel feature interaction mechanism and an adaptive ensemble learning approach. The proposed method enables segmentation by leveraging recent attention mechanisms, dedicated decoders, and uncertainty estimation techniques. The proposed model was extensively evaluated using the BraTS2019-2021 datasets, achieving an outstanding performance with mean Dice scores of 0.91, 0.87, and 0.83 on whole tumor, tumor core and enhancing tumor regions respectively. The proposed architecture achieves stable performance over a range of tumor types and sizes, with low relative computational cost.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:dbk:datame:v:4:y:2025:i::p:374:id:1056294dm2025374

DOI: 10.56294/dm2025374

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