Automated Identification and Segmentation of Liver Tumors in Computed Tomography via Deep Models | ||
| IJCI. International Journal of Computers and Information | ||
| Articles in Press, Accepted Manuscript, Available Online from 14 September 2025 | ||
| Document Type: Original Article | ||
| DOI: 10.21608/ijci.2025.402195.1202 | ||
| Authors | ||
| asmaa sabet anwar* 1; Khalid Amin2; Mohiy hadhood2; mina ibrahim3 | ||
| 1Department of Computer Engineering, Faculty of Engineering, May University, Cairo,Egypt | ||
| 2Information Technology dept., Faculty of Computers and Information, Menoufia University, Egypt | ||
| 3Department of Machine Intelligence, Faculty of Artificial Intelligence, Menoufia University, Egypt | ||
| Abstract | ||
| Precise segmentation of liver and tumor regions in computed tomography (CT) images is crucial for accurate diagnosis, treatment planning, and surgical guidance. This study proposes a Hybrid DenseNet-U-Net model specifically designed for liver and tumor segmentation. The architecture integrates DenseNet121 as the encoder backbone to exploit dense connectivity for enhanced feature reuse and efficient gradient propagation, while the U-Net decoder performs systematic upsampling with skip connections to preserve spatial details. Additional components such as batch normalization, dropout layers, and optimized filter configurations are incorporated to improve segmentation accuracy and robustness. The model was evaluated on the LiTS and 3D-IRCADb-01 datasets, demonstrating strong performance in segmenting liver and tumor structures. Results indicate that the proposed method achieves high accuracy and robustness across both datasets, outperforming several existing architectures in capturing complex anatomical boundaries. The use of data augmentation techniques, including rotations and flips, enhances generalization to diverse imaging conditions. Owing to its relatively low parameter count and efficient resource utilization, the model is well-suited for deployment in real-time or resource-constrained clinical environments. These findings highlight the model’s potential as a dependable and computationally efficient tool for automated liver and tumor segmentation in CT imaging, paving the way for broader adoption in clinical workflows and future extension to other anatomical segmentation tasks. | ||
| Keywords | ||
| Tumor Segmentation; U-Net; Deep learning; DenseNet, transfer learning | ||
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