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Pneumonia III: Complications and Assessment01:30

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Pneumonia poses the potential for numerous complications that warrant consideration. These complications include the following:
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Pneumothorax-II01:27

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Pneumothorax is a medical condition defined by the buildup of air in the pleural space between the lungs and the chest wall. This accumulation of air can lead to partial or complete lung collapse, resulting in a range of clinical manifestations. Understanding the clinical presentation and effective management strategies is crucial for healthcare professionals in providing timely and appropriate care to individuals with pneumothorax.
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Efficient Pneumonia Detection in Chest Xray Images Using Deep Transfer Learning.

Mohammad Farukh Hashmi1, Satyarth Katiyar2, Avinash G Keskar3

  • 1Department of Electronics and Communication Engineering, National Institute of Technology, Warangal 506004, India.

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|June 25, 2020
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Summary
This summary is machine-generated.

This study introduces an efficient deep learning model for detecting pneumonia from chest X-rays, significantly improving diagnostic accuracy. The novel weighted classifier achieved 98.43% test accuracy, aiding radiologists in faster pneumonia diagnosis.

Keywords:
chest X-ray imagescomputer-aided diagnosticsconvolution neural network (CNN)deep learningpneumoniatransfer learning

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer-Aided Diagnosis

Background:

  • Pneumonia affects millions globally, causing high child mortality.
  • Chest X-ray interpretation is challenging, necessitating improved diagnostic tools.
  • Current diagnostic methods require enhancement for accuracy and efficiency.

Purpose of the Study:

  • To develop an efficient deep learning model for accurate pneumonia detection using digital chest X-rays.
  • To create a weighted classifier that integrates predictions from multiple state-of-the-art deep learning models.
  • To provide a tool that assists radiologists in the pneumonia diagnosis process.

Main Methods:

  • A novel weighted classifier combining ResNet18, Xception, InceptionV3, DenseNet121, and MobileNetV3 predictions.
  • Supervised learning approach utilizing transfer learning for model fine-tuning.
  • Data augmentation techniques to balance and expand the training dataset.

Main Results:

  • The proposed weighted classifier outperformed individual deep learning models.
  • Achieved a test accuracy of 98.43% on unseen data.
  • Obtained an Area Under the Curve (AUC) score of 99.76%.

Conclusions:

  • The developed weighted classifier model demonstrates high efficacy for pneumonia detection.
  • The model can significantly aid radiologists in making quicker and more accurate diagnoses.
  • This AI-driven approach shows promise for improving patient outcomes in pneumonia cases.