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Binary Classification of Pneumonia in Chest X-Ray Images Using Modified Contrast-Limited Adaptive Histogram

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Summary
This summary is machine-generated.

This study introduces an adaptive contrast enhancement model and convolutional neural network (CNN) for accurate pneumonia detection in chest X-rays. The combined approach achieved 98.7% accuracy, improving diagnostic support.

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CNNimage enhancementmodified CLAHEpneumonia classification

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • Pneumonia is a significant global health issue requiring advanced diagnostic methods.
  • Automated analysis of chest X-rays can aid in timely pneumonia detection.
  • Existing methods may lack the precision needed for reliable automated diagnosis.

Purpose of the Study:

  • To develop and evaluate a novel approach for the binary classification of pneumonia using chest X-ray images.
  • To enhance image quality for improved pneumonia detection through an adaptive contrast enhancement model.
  • To assess the performance of a convolutional neural network (CNN) integrated with the enhancement model.

Main Methods:

  • Implementation of an adaptive contrast enhancement model featuring adaptive tile sizing, variance-guided clipping, and entropy-weighted redistribution.
  • Application of the enhancement model to the Chest X-Ray Images (Pneumonia) dataset (5856 images).
  • Training and evaluation of a convolutional neural network (CNN) on the enhanced images for pneumonia classification.

Main Results:

  • The proposed model achieved high classification performance: 98.7% accuracy, 99.3% precision, 98.6% recall, and 97.9% F1-score.
  • The adaptive enhancement significantly improved CNN performance compared to baseline methods.
  • Five-fold cross-validation confirmed the model's robustness, and feature visualization indicated clinical relevance.

Conclusions:

  • The integrated adaptive contrast enhancement and CNN approach offers a reliable method for automated pneumonia classification from chest X-rays.
  • This technique shows potential for enhancing diagnostic support systems in medical imaging.
  • Future work will focus on validating the model's generalizability on larger, diverse datasets.