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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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A ResNet-50-UNet Hybrid with Whale Optimization Algorithm for Accurate Liver Tumor Segmentation.

Proloy Kumar Mondol1, Md Ariful Islam Mozumder1,2, Hee Cheol Kim1

  • 1Institute of Digital Anti-Aging Healthcare, Inje University, Gimhae-si 50834, Republic of Korea.

Diagnostics (Basel, Switzerland)
|December 11, 2025
PubMed
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This summary is machine-generated.

This study introduces a hybrid deep learning model combining U-Net and Whale Optimization Algorithm (WOA) for accurate liver tumor segmentation. The novel approach significantly improves segmentation accuracy, aiding in liver cancer diagnosis and treatment planning.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Accurate segmentation of liver and liver tumors from 3D medical images is crucial for liver cancer diagnosis and treatment planning.
  • Challenges in segmentation arise from organs with similar characteristics, leading to difficulties in precise delineation.

Purpose of the Study:

  • To develop a hybrid deep learning model for enhanced segmentation of liver and liver tumors.
  • To optimize deep learning model hyperparameters using the Whale Optimization Algorithm (WOA) for improved segmentation performance.

Main Methods:

  • A hybrid model integrating a U-Net based structure with the Whale Optimization Algorithm (WOA) was proposed.
  • WOA was employed to fine-tune the hyperparameters of the LiTS-Res-UNet architecture for optimal deep learning model performance.
Keywords:
LiTs-UNet-WOAResNet-50UNetWhale Optimization Algorithmbio-inspired optimizationdeep learningmetaheuristic optimization

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Main Results:

  • The LiTS-Res-Unet + WOA hybrid model achieved high accuracy (99.54%), Dice coefficient (92.38%), and Jaccard index (86.73%) on a benchmark dataset.
  • The proposed model outperformed existing state-of-the-art methods in liver tumor segmentation tasks.

Conclusions:

  • The WOA-based adaptive search effectively identified optimal hyperparameters, enhancing deep learning model convergence and accuracy.
  • The hybrid model demonstrated robust performance and clinical applicability for precise liver tumor segmentation.