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

• Author(s): Dandagula Jagadeesh; Chitla Vignesh; Vaddelli Srinivas Rao
• Paper ID: 1718946
• Page: 2016-2019
• Published Date: 18-06-2026
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 12 June-2026

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

Citations

IRE Journals:
Dandagula Jagadeesh, Chitla Vignesh, Vaddelli Srinivas Rao "Hybrid Transformer-CNN Framework for Multi-Organ Tumor Segmentation in Abdominal CT Scans" Iconic Research And Engineering Journals Volume 9 Issue 12 2026 Page 2016-2019 https://doi.org/10.64388/IREV9I12-1718946

IEEE:
Dandagula Jagadeesh, Chitla Vignesh, Vaddelli Srinivas Rao "Hybrid Transformer-CNN Framework for Multi-Organ Tumor Segmentation in Abdominal CT Scans" Iconic Research And Engineering Journals, 9(12) https://doi.org/10.64388/IREV9I12-1718946