Investigating the impact of data fusion on the ResUNet model for liver tumor segmentation Karaciğer tümör segmentasyonu için veri füzyonunun ResUNet modeli üzerindeki etkisinin araştırılması
Journal of the Faculty of Engineering and Architecture of Gazi University, cilt.41, sa.1, ss.533-547, 2026 (SCI-Expanded, Scopus, TRDizin)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 41 Sayı: 1
- Basım Tarihi: 2026
- Doi Numarası: 10.17341/gazimmfd.1708157
- Dergi Adı: Journal of the Faculty of Engineering and Architecture of Gazi University
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Art Source, Compendex, TR DİZİN (ULAKBİM), Academic Search Ultimate (EBSCO), Engineering Source (EBSCO)
- Sayfa Sayıları: ss.533-547
- Anahtar Kelimeler: discrete wavelet transform image fusion, liver tumor segmentation, Medical image segmentation, principal component analysis image fusion, ResUNet model
- Yozgat Bozok Üniversitesi Adresli: Evet
Özet
Medical image segmentation is employed to separate regions in images based on color and shape differences for disease diagnosis or localization of pathological areas. It can be performed manually or automatically. Automatic segmentation methods leverage machine learning and deep learning techniques, with domain-specific models developed to enhance performance; U-Net-based architectures can achieve effective results even with limited and imbalanced medical datasets. However, U-Net models may face training challenges such as vanishing gradients in deep networks, which can be addressed by ResNet architectures providing deeper structures. The hybrid ResUNet combines the segmentation capabilities of U-Net with the residual connections of ResNet, thus exploiting the advantages of deep networks while mitigating training difficulties. In this study, for automatic liver tumor segmentation, the hybrid ResUNet was applied to channel-based fused data obtained using Principal Component Analysis (PCA) and Discrete Wavelet Transform (DWT). Channel-based data fusion preserves the unique and distinctive patterns of each channel, enriching feature representations, and both PCA- and DWT-based fusion methods transform the data into different spaces, enhancing the model's ability to differentiate various structures. The results demonstrate that both methods achieve comparable performance, yielding similar dice similarity coefficient values across two different datasets.