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Analyzing Pulmonary Abnormality with Superpixel Based Graph Neural Network in Chest X-Ray

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Recent Trends in Image Processing and Pattern Recognition (RTIP2R 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2027))

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Abstract

In recent years, the utilization of graph-based deep learning has gained prominence, yet its potential in the realm of medical diagnosis remains relatively unexplored. The challenge arises from the inherent irregular and unordered nature of physiological data, making it challenging to depict intricate patterns using conventional methods. Our research focuses on abnormality screening: classification of Chest X-Ray (CXR) as Tuberculosis positive or negative, using Graph Neural Networks (GNN) that uses Region Adjacency Graphs (RAGs) and each superpixel serves as a dedicated graph node. By integrating residual and concatenation structures, our approach effectively captures crucial features and relationships among superpixels, facilitating advancements in tuberculosis identification. Through the amalgamation of state-of-the-art neural network architectures and innovative graph-based representations, our work introduces a new perspective to medical image analysis.

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Authors and Affiliations

  1. Applied Artificial Intelligence Research Lab, Department of Computer Science, University of South Dakota, Vermillion, SD, 57069, USA

    Ronaj Pradhan & KC Santosh

Authors
  1. Ronaj Pradhan
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  2. KC Santosh
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Corresponding author

Correspondence to KC Santosh .

Editor information

Editors and Affiliations

  1. University of South Dakota, Vermillion, SD, USA

    KC Santosh

  2. University of Derby, Derby, UK

    Aaisha Makkar

  3. University of Derby, Derby, UK

    Myra Conway

  4. Indian Institute of Information Technology, Bhopal, India

    Ashutosh K. Singh

  5. University of Clermont Auvergne, Aubière, France

    Antoine Vacavant

  6. ENSA - Cadi Ayyad University, Marrakesh, Morocco

    Anas Abou el Kalam

  7. Halmstad University, Halmstad, Sweden

    Mohamed-Rafik Bouguelia

  8. Central University of Karnataka, Kalaburagi, India

    Ravindra Hegadi

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Pradhan, R., Santosh, K. (2024). Analyzing Pulmonary Abnormality with Superpixel Based Graph Neural Network in Chest X-Ray. In: Santosh, K., et al. Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2023. Communications in Computer and Information Science, vol 2027. Springer, Cham. https://doi.org/10.1007/978-3-031-53085-2_9

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  • DOI: https://doi.org/10.1007/978-3-031-53085-2_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-53084-5

  • Online ISBN: 978-3-031-53085-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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