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Concatenated CNN-Based Pneumonia Detection Using a Fuzzy-Enhanced Dataset.

Abror Shavkatovich Buriboev1,2, Dilnoz Muhamediyeva3, Holida Primova4

  • 1School of Computing, Department of AI-Software, Gachon University, Seongnam-si 13306, Republic of Korea.

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Summary

This study introduces a novel Concatenated CNN model combined with fuzzy logic image enhancement for improved pneumonia detection. The enhanced model achieved high accuracy, precision, and recall, offering a faster and more effective diagnostic tool.

Keywords:
chest X-ray imageconcatenated CNNimage enhancementpneumonia detection

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Respiratory Medicine

Background:

  • Pneumonia diagnosis is challenging due to similar viral and bacterial symptoms.
  • Current reliable methods like PCR require hours and specialized conditions.
  • Need for rapid, accurate, and accessible diagnostic tools is critical.

Purpose of the Study:

  • To develop an advanced deep learning model for pneumonia detection.
  • To enhance medical image quality using fuzzy logic for improved diagnostic accuracy.
  • To integrate fuzzy logic-based image enhancement with a Concatenated CNN (CCNN) model.

Main Methods:

  • A novel fuzzy logic-based image refinement algorithm was developed.
  • The algorithm improved image quality and feature extraction for the CCNN model.
  • Four datasets (original and enhanced images) were used to train the CCNN.

Main Results:

  • The CCNN model demonstrated significantly improved performance with enhanced datasets.
  • The fuzzy entropy-enhanced dataset yielded the best results.
  • Achieved high classification metrics: 98.9% accuracy, 99.3% precision, 99.8% F1-score, 99.6% recall.

Conclusions:

  • Fuzzy logic-based image enhancement substantially improves CCNN model performance for pneumonia detection.
  • The proposed method outperforms traditional image enhancement techniques.
  • This integrated approach offers a promising solution for precise and efficient medical image analysis.