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A deep community based approach for large scale content based X-ray image retrieval.

Nandinee Fariah Haq1, Mehdi Moradi2, Z Jane Wang1

  • 1The University of British Columbia, Vancouver, Canada.

Medical Image Analysis
|November 29, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a novel deep learning framework for retrieving similar medical images from large X-ray databases. The automated system enhances clinical decision support by accurately identifying comparable images with 85% precision.

Keywords:
Community detectionContent based image retrieval (CBIR)Deep learningGraph

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Science

Background:

  • Computer-assisted systems aid in managing large medical image datasets.
  • Similar image retrieval is valuable for clinical decision support.
  • Existing techniques for medical image retrieval require improvement.

Purpose of the Study:

  • To propose a deep community-based automated framework for retrieving similar images from large-scale X-ray databases.
  • To enhance the efficiency and accuracy of medical image retrieval systems.

Main Methods:

  • Integration of a deep learning-based image feature generation approach.
  • Application of a network community detection technique for image similarity extraction.
  • Evaluation on two large-scale chest X-ray datasets.

Main Results:

  • The proposed framework demonstrated improved performance compared to state-of-the-art techniques.
  • Achieved 85% precision in extracting images with similar disease labels from a query image.
  • This represents the first deep community-based image retrieval application on a large-scale chest X-ray database.

Conclusions:

  • The developed framework is effective for automated medical image retrieval.
  • The system shows significant potential for clinical decision support and data mining in radiology.
  • Further research can explore broader applications of this deep community-based approach.