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A Modified LBP Operator-Based Optimized Fuzzy Art Map Medical Image Retrieval System for Disease Diagnosis and

Anitha K1, Radhika S2, Kavitha C3

  • 1Department of Computing Technologies, School of Computing, College of Engineering and Technology, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur, Chennai 603203, India.

Biomedicines
|October 27, 2022
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Summary

This study introduces an advanced medical image retrieval system using a novel feature extraction method and an optimized classifier. The proposed framework enhances classification accuracy and efficiency for medical image analysis.

Keywords:
DEFAMNetFAM classifiersLBP variantsfeature indexingimage retrieval

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Area of Science:

  • * Medical Informatics
  • * Computer Vision
  • * Machine Learning

Background:

  • * Medical records are crucial for research and clinical reference.
  • * Efficient Medical Image Retrieval (MIR) systems are needed for diagnosis and treatment.
  • * Current systems require improved classification and indexing techniques.

Purpose of the Study:

  • * To develop an efficient framework for medical image classification and retrieval.
  • * To enhance the accuracy and speed of medical image analysis.
  • * To reduce the computational cost associated with medical image processing.

Main Methods:

  • * A modified Local Binary Pattern (AvN-LBP) feature for robust image indexing.
  • * An optimized Fuzzy Art Map (FAM) network, termed DEFAMNet, using Differential Evolution (DE) for classification.
  • * Integration of AvN-LBP and DEFAMNet for a comprehensive retrieval framework.

Main Results:

  • * The AvN-LBP operator demonstrated robustness to background noise.
  • * DEFAMNet achieved higher classification accuracy compared to other methods.
  • * The proposed framework significantly improved classification speed and efficiency.

Conclusions:

  • * The developed AvN-LBP and DEFAMNet framework offers a superior solution for medical image retrieval.
  • * The system provides faster, more efficient, and accurate medical image classification.
  • * This approach reduces computational costs, making it valuable for clinical applications.