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This study introduces an intelligent healthcare system using IoT and deep learning for rapid COVID-19 and pneumothorax detection from X-rays. The system achieves high accuracy and near real-time analysis, aiding in proactive disease diagnosis.

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COVID-19chest X-ray scansdecision support systemdeep leaningpneumothorax

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Healthcare
  • Infectious Disease Diagnostics

Background:

  • Growing interest in smart remote patient monitoring and healthcare technology.
  • Need for advanced diagnostic tools for infectious diseases like COVID-19.
  • Integration of deep learning in medical scan analysis is increasing.

Purpose of the Study:

  • To develop an intelligent medicare system for automated detection and categorization of infectious diseases.
  • To create an IoT-empowered, deep learning-based decision support system (DSS).
  • To specifically target COVID-19 and pneumothorax detection using chest X-rays.

Main Methods:

  • Utilized an IoT-empowered, deep learning-based decision support system (DSS).
  • Evaluated the DSS using three independent, standard-based chest X-ray scan datasets.
  • Developed a predictor to identify and classify abnormalities indicative of COVID-19 and pneumothorax.

Main Results:

  • Achieved an identification and classification accuracy rate of 89.58% for normal images.
  • Reached an accuracy rate of 89.13% for COVID-19 and pneumothorax detection.
  • The DSS provides diagnoses in approximately 0.01 seconds per scan, operating at 95 frames per second (FPS).

Conclusions:

  • The proposed DSS demonstrates high accuracy in detecting COVID-19 and pneumothorax from chest X-rays.
  • The system's speed enables near real-time analysis, suitable for epidemiological scenarios.
  • This intelligent medicare system offers a proactive diagnostic solution for infectious diseases.