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All Journal Bulletin of Electrical Engineering and Informatics
Ala Eddin Sadeq
Zarqa University
Electrical & Electronics Engineering

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Hybrid dual-stream deep learning for breast cancer ultrasound detection Musab Mahmoud Iqtait; Marwan Harb Alqaryouti; Ala Eddin Sadeq; Jafar Ababneh; Suhaila Abuowaida; Nawaf Alshdaifat; Muath Alali
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.10102

Abstract

The heterogeneity of breast tissue and subtle morphological variations in ultrasound images make breast cancer detection a challenging task. This study proposes a hybrid deep learning framework that integrates EfficientNetB4 and ConvNeXt within a dual-stream architecture enhanced by advanced attention mechanisms. The model combines multi-scale texture representation with spatial feature extraction to improve classification performance. A two-stage preprocessing pipeline, consisting of adaptive median filtering and bilateral filtering, is applied to reduce speckle noise while preserving important structural details. The proposed method is evaluated on BUSI and UDAIT datasets, achieving 87.82% accuracy, 87.33% precision, and 85.33% recall on BUSI, and 85.69% accuracy, 84.00% precision, and 78.00% recall on UDAIT. These results outperform several baseline models, including ResNet-50, DenseNet-121, and vision transformers. Error analysis shows limitations in detecting small lesions and cross-modal generalization, with reduced performance on mammography images. Attention visualization demonstrates strong agreement with radiologist annotations, supporting model interpretability. The findings highlight the effectiveness of hybrid architectures for ultrasound-based breast cancer detection while emphasizing the need for modality-specific optimization.
Co-Authors Jafar Ababneh Marwan Harb Alqaryouti Muath Alali Musab Mahmoud Iqtait Nawaf Alshdaifat Suhaila Abuowaida