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Lung nodule segmentation using Salp Shuffled Shepherd Optimization Algorithm-based Generative Adversarial Network.

Supiksha Jain1, Sanjeev Indora1, Dinesh Kumar Atal2

  • 1Department of Computer Science and Engineering, Deenbandhu Chhotu Ram University of Science and Technology, Murthal, Sonipat, Haryana, India.

Computers in Biology and Medicine
|September 7, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Salp Shuffled Shepherd Optimization Algorithm-based Generative Adversarial Network (SSSOA-based GAN) for accurate lung nodule segmentation. The SSSOA-based GAN model significantly improves accuracy in detecting lung cancer from CT images.

Keywords:
Generative adversarial networkImage processingLung lobe segmentationLung nodule segmentation

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computational Biology

Background:

  • Lung nodule segmentation is crucial for early lung cancer detection.
  • Visual deviations and heterogeneity in lung nodules pose significant challenges to segmentation accuracy.
  • Existing methods struggle with the accuracy required for effective lung cancer diagnosis.

Purpose of the Study:

  • To develop an advanced model for precise lung nodule segmentation.
  • To enhance the accuracy of lung cancer detection using improved segmentation techniques.
  • To address the limitations of current methods in handling nodule heterogeneity.

Main Methods:

  • A hybrid optimization algorithm, Salp Shuffled Shepherd Optimization Algorithm (SSSOA), was developed by integrating the Salp Swarm Algorithm (SSA) and shuffled shepherd optimization algorithm (SSOA).
  • A Generative Adversarial Network (GAN) was employed for lung nodule segmentation, trained using the SSSOA.
  • Image pre-processing involved Gaussian filtering to remove artefacts, followed by deep joint segmentation for lung lobe identification.

Main Results:

  • The SSSOA-based GAN model achieved a maximum Accuracy of 0.9387.
  • The model demonstrated a maximum Dice Coefficient of 0.7986.
  • A maximum Jaccard Similarity of 0.8026 was obtained, outperforming existing lung nodule segmentation methods.

Conclusions:

  • The developed SSSOA-based GAN model offers a significant advancement in lung nodule segmentation accuracy.
  • This approach shows promise for improving the early detection and diagnosis of lung cancer.
  • The hybrid optimization strategy effectively enhances the performance of GANs in medical image segmentation.