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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics
Suhaila Abuowaida
Al al-Bayt University
Computer Science & IT 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.
A comprehensive analysis of eye diseases and medical data classification Raed Alazaidah; Hamza Abu Owida; Nawaf Alshdaifat; Abedalhakeem Issa; Suhaila Abuowaida; Nidal Yousef
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 6: December 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i6.26058

Abstract

Vision loss is a critical health issue that presents substantial challenges to both individuals and communities. For those affected, it can lead to difficulties in performing daily activities, hinder educational and employment opportunities, and significantly impact mental health and overall quality of life. The inability to see can also lead to increased dependence on others, creating emotional and financial strains on families and caregivers. This paper highlights the benefit of machine learning (ML) in exploring conditions that significantly affect vision loss. The goals that will be achieved in this paper are to determine the best classifier capable of dealing with medical datasets and to determine the best strategy for dealing with medical data. Determine which feature selection is most applicable to use for examining medical data. Two medical datasets, 4 strategies, 19 classifiers, and 2 feature selections were used. As for the best classifier, the stochastic gradient descent (SGD) model was the best in dataset 1 and 2. The function strategy showed the best performance, followed by the rules strategy. CorrelationAttributeEval was shown to be the best feature selection, while ClassifierAttributeEval was the second-best feature selection.
Co-Authors Abedalhakeem Issa Ala Eddin Sadeq Hamza Abu Owida Jafar Ababneh Marwan Harb Alqaryouti Muath Alali Musab Mahmoud Iqtait Nawaf Alshdaifat Nawaf Alshdaifat Nidal Yousef Raed Alazaidah