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Updated: Jun 27, 2026

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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DPCrossU-Net: a dual-branch parallel CNN-Transformer network for lung nodule segmentation.

Xiya Guan1, Wen Zhu2, Fangxiang Wu3

  • 1School of Mathematics and Statistics, Hainan Normal University, Haikou, China.

Frontiers in Oncology
|June 26, 2026
PubMed
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Accurate lung nodule segmentation is improved with DPCrossU-Net, a novel dual-branch network. This method effectively combines convolutional neural networks (CNNs) and Vision Transformers (ViTs) for enhanced early lung cancer detection.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer-Aided Diagnosis

Background:

  • Accurate lung nodule segmentation in CT images is critical for early lung cancer detection and diagnosis.
  • Existing segmentation models struggle with small nodules, complex boundaries, and balancing local/global feature extraction.

Purpose of the Study:

  • To develop an advanced deep learning model for precise lung nodule segmentation.
  • To improve the accuracy and robustness of lung nodule segmentation in CT scans.

Main Methods:

  • Proposed DPCrossU-Net, a dual-branch parallel encoder-decoder network integrating CNN and Vision Transformer (ViT) features.
  • Employed a Cross-Attentive Fusion (CAF) module for adaptive combination of local texture and global semantic information.
  • Incorporated multi-scale atrous convolutions and a dual-branch Detail Context Fusion (DCF) block for enhanced small nodule sensitivity and boundary reconstruction.
Keywords:
CNN–Transformer hybrid modelLIDC-IDRIdual-branch architecturefeature fusionlung nodule segmentation

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Main Results:

  • DPCrossU-Net achieved a Dice score of 85.89% on the LIDC-IDRI dataset.
  • Outperformed baseline U-Net, demonstrating superior performance in segmenting small nodules and complex cases.
  • Showcased effectiveness in handling challenging segmentation scenarios with intricate boundaries and varied nodule sizes.

Conclusions:

  • Synergistic combination of parallel CNN-Transformer feature extraction and adaptive fusion significantly enhances lung nodule segmentation accuracy.
  • DPCrossU-Net offers a robust and clinically applicable solution for improved early lung cancer analysis.
  • The model holds potential for supporting future intelligent diagnostic systems in radiology.