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Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
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Lung Disease Detection Using U-Net Feature Extractor Cascaded by Graph Convolutional Network.

Pshtiwan Qader Rashid1, İlker Türker1

  • 1Department of Computer Engineering, Karabuk University, 78050 Karabuk, Turkey.

Diagnostics (Basel, Switzerland)
|June 27, 2024
PubMed
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This study introduces a novel feature-extracted graph convolutional network (FGCN) for diagnosing lung diseases from CT scans. The FGCN model significantly improves accuracy by capturing spatial connectivity, outperforming traditional deep learning methods.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Graph Neural Networks

Background:

  • Computed tomography (CT) scans are crucial for rapid lung disease diagnosis.
  • Existing deep learning methods often overlook spatial connectivity in CT image analysis.

Purpose of the Study:

  • To develop an accurate method for COVID-19 diagnosis using graph convolutional networks (GCNs).
  • To enhance feature extraction from CT scans by incorporating spatial connectivity patterns.

Main Methods:

  • Utilized U-Net for image segmentation and feature extraction.
  • Employed GCNs to capture spatial connectivity from extracted deep features, forming an adjacency matrix.
  • Integrated original image graph, largest kernel graph, and feature-extracted graph for input to GCN.
Keywords:
COVID-19deep learninggraph convolutional networksgraph representative learninglung disease detection

Related Experiment Videos

Last Updated: May 5, 2026

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
05:56

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  • Applied a dropout layer to mitigate overfitting.

    • Main Results:

    • The proposed feature-extracted graph convolutional network (FGCN) demonstrated superior performance in lung disease identification.
    • FGCN outperformed existing deep learning architectures not based on graph representations.
    • The model also surpassed common transfer learning models used in medical diagnosis.

    Conclusions:

    • Graph representation offers significant advantages over traditional methods for medical image analysis.
    • The FGCN framework provides a powerful tool for accurate and efficient lung disease diagnosis from CT scans.