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Related Concept Videos

Pneumonia III: Complications and Assessment01:30

Pneumonia III: Complications and Assessment

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Pneumonia poses the potential for numerous complications that warrant consideration. These complications include the following:
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Clinical Manifestations:
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Pneumonia detection on chest X-rays from Xception-based transfer learning and logistic regression.

Muhammad Mujahid1, Furqan Rustam2, Prasun Chakrabarti3

  • 1Artificial Intelligence & Data Analytics Lab, CCIS, Prince Sultan University, Riyadh 11586, Saudi Arabia.

Technology and Health Care : Official Journal of the European Society for Engineering and Medicine
|November 9, 2024
PubMed
Summary

This study enhances pneumonia detection from chest X-rays using machine learning and data augmentation. Transfer learning with Xception and VGG-16 achieved 99.23% accuracy, improving upon traditional methods.

Keywords:
COVID-19Pneumonia predictionautomatic feature extractionchest radiographstransfer learning

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Machine Learning

Background:

  • Pneumonia is a leading cause of death globally, particularly in children and the elderly.
  • Chest X-rays are a primary diagnostic tool, but interpretation by radiologists is time-consuming and prone to errors, especially with overlapping symptoms with other conditions like COVID-19.
  • Current machine learning models for pneumonia detection from X-rays struggle with data imbalance and generalization, limiting their accuracy and robustness.

Purpose of the Study:

  • To improve the accuracy and robustness of machine learning models for pneumonia detection from chest X-rays.
  • To address data imbalance issues through data augmentation techniques.
  • To evaluate the effectiveness of transfer learning for automatic feature extraction compared to traditional hand-crafted features.

Main Methods:

  • Utilized transfer learning with pre-trained models (Xception and VGG-16) for automatic feature extraction from chest X-rays.
  • Employed data augmentation to mitigate data imbalance problems.
  • Trained and compared various classifiers, including Support Vector Machine, Logistic Regression, K-Nearest Neighbor, Stochastic Gradient Descent, Extra Tree Classifier, and Gradient Boosting Machine.

Main Results:

  • Transfer learning-based features demonstrated superior performance compared to hand-crafted features for pneumonia detection.
  • Achieved a high accuracy of 99.23% for pneumonia detection using chest X-rays with the proposed transfer learning approach.
  • The study successfully addressed data imbalance issues, leading to more robust model performance.

Conclusions:

  • Transfer learning offers a powerful approach for automatic feature extraction in medical image analysis, significantly enhancing pneumonia detection accuracy.
  • Data augmentation is crucial for improving the performance of machine learning models dealing with imbalanced datasets in medical diagnostics.
  • The developed method provides a highly accurate and efficient automated solution for pneumonia detection from chest X-rays, potentially aiding clinical decision-making.