Hybrid Transformer-CNN Framework for Multi-Organ Tumor Segmentation in Abdominal CT Scans

@article{1718946,
      author = {Dandagula Jagadeesh, Chitla Vignesh, Vaddelli Srinivas Rao},
      title = {Hybrid Transformer-CNN Framework for Multi-Organ Tumor Segmentation in Abdominal CT Scans},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {12},
      pages = {2016-2019},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1718946.pdf},
      abstract = {Accurate segmentation of multiple abdominal organs and co-occurring tumors in CT imaging presents a significant challenge due to high inter-patient anatomical variability, low contrast boundaries, and class imbalance. This paper proposes HybridSegNet, a novel architecture that integrates a Swin Transformer encoder with a multi-scale convolutional decoder equipped with dense skip connections and a dual-branch feature fusion module. HybridSegNet is evaluated on the publicly available CHAOS dataset (liver, kidney segmentation) and the KiTS23 challenge dataset (kidney tumor segmentation). The model achieves a mean Dice Similarity Coefficient (DSC) of 0.913 on liver segmentation, 0.897 on kidney segmentation, and 0.884 on kidney tumor segmentation, outperforming leading methods including nnU-Net (DSC: 0.876) and Swin-UNet (DSC: 0.861). A lightweight model variant is also proposed for deployment on resource-constrained clinical workstations without significant performance degradation. Results demonstrate HybridSegNet's suitability for real-time clinical decision support in abdominal oncology.},
      keywords = {Abdominal CT, Deep Learning, Feature Fusion, Medical Image Segmentation, Multi-Organ Segmentation, Swin Transformer, Tumor Detection},
      month = {June},
      doi = {https://doi.org/10.64388/IREV9I12-1718946}
  }