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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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Pneumonia IV: Management01:28

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The treatment of pneumonia varies based on its severity and the causative pathogen. Here is a structured approach to managing pneumonia, integrating pharmaceutical and supportive care strategies.
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Pneumonia is an acute respiratory infection that targets the lungs, specifically the alveoli. These tiny air sacs, essential for oxygen exchange, become engorged with pus and fluid, severely hindering breathing, decreasing oxygen absorption, and causing significant pain and discomfort during respiration.
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Ensemble of Deep Learning Architectures with Machine Learning for Pneumonia Classification Using Chest X-rays.

Rupali Vyas1, Deepak Rao Khadatkar2

  • 1Department of Computer Science and Engineering, Shri Shankaracharya Institute of Professional Management and Technology, Raipur, C.G, India. rupalivyas1996@gmail.com.

Journal of Imaging Informatics in Medicine
|August 14, 2024
PubMed
Summary

This study introduces deep learning combined with machine learning classifiers (DLxMLCs) for accurate pneumonia classification from chest X-ray images. The DLxMLC approach achieved over 99% accuracy, enhancing diagnostic speed and efficiency.

Keywords:
Chest X-rayDenseNet121Ensemble methodsPneumoniaResNet50V2VGG19

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Healthcare
  • Computational Pathology

Background:

  • Pneumonia poses a significant health risk, especially to vulnerable populations, necessitating prompt and accurate diagnosis for effective treatment.
  • Chest X-ray (CXR) imaging is a primary diagnostic tool for pneumonia, but interpretation can be challenging and time-consuming.

Purpose of the Study:

  • To evaluate the efficacy of combining deep learning (DL) models with machine learning (ML) classifiers for automated pneumonia classification from CXR images.
  • To assess the diagnostic accuracy and efficiency of a novel deep learning combined with machine learning classifiers (DLxMLC) approach.

Main Methods:

  • Utilized modified VGG19, ResNet50V2, and DenseNet121 deep learning models for automated feature extraction from CXR images.
  • Integrated extracted features with five distinct machine learning classifiers: logistic regression, support vector machine, decision tree, random forest, and artificial neural network.
  • Evaluated the performance of various DLxMLC combinations for pneumonia detection.

Main Results:

  • The VGG19 and DenseNet121 models, when paired with random forest or decision tree classifiers, achieved an exceptional accuracy of 99.98%.
  • The ResNet50V2 model demonstrated 99.25% accuracy when combined with the random forest classifier.
  • These high accuracies highlight the effectiveness of the proposed DLxMLC strategy in pneumonia identification.

Conclusions:

  • The integration of deep learning models with machine learning classifiers significantly enhances the speed and accuracy of pneumonia detection from CXR images.
  • DLxMLC systems show considerable potential for improving diagnostic accuracy and operational efficiency in clinical settings.
  • Further research is recommended to refine these models, explore their application in other medical imaging domains, and incorporate explainability for clinical trust and adoption.