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Interactive framework for Covid-19 detection and segmentation with feedback facility for dynamically improved

Kashfia Sailunaz1, Deniz Bestepe2, Tansel Özyer3

  • 1Department of Computer Science, University of Calgary, Calgary, Alberta, Canada.

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|December 22, 2022
PubMed
Summary
This summary is machine-generated.

A new web application framework aids in the rapid detection and segmentation of COVID-19 lung infections using deep learning models. The U-Net model demonstrated superior performance, achieving over 98% accuracy in identifying COVID-19 from CT scans.

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Area of Science:

  • Artificial Intelligence
  • Medical Imaging
  • Deep Learning

Background:

  • Accurate and rapid diagnosis of COVID-19 is critical for pandemic containment.
  • Deep learning (DL) and transfer learning (TL) show promise for automated detection and segmentation of infections from medical images.
  • Existing AI approaches often utilize Convolutional Neural Networks (CNNs) and Deep Neural Networks (DNNs).

Purpose of the Study:

  • To develop a web-based application framework for detecting and segmenting COVID-19 lung infections.
  • To implement and evaluate popular DL models, including Mask R-CNN, U-Net, and U-Net++, within the framework.
  • To incorporate a feedback mechanism for self-learning and model tuning.

Main Methods:

  • A web application framework was designed with a user-friendly interface.
  • Variations of Mask R-CNN, U-Net, and U-Net++ models were employed for detection and segmentation.
  • Models were trained, evaluated, and tested using Computed Tomography (CT) images from two distinct patient data sources.

Main Results:

  • The developed framework successfully processed CT images for COVID-19 detection and segmentation.
  • All implemented DL models achieved high performance metrics, including Dice similarity, Jaccard similarity, accuracy, loss, and precision.
  • The U-Net model exhibited the highest performance, surpassing 98% accuracy.

Conclusions:

  • The proposed web application framework offers an effective tool for COVID-19 lung infection detection and segmentation.
  • Deep learning models, particularly U-Net, show significant potential for accurate and efficient analysis of medical images for pandemic diagnosis.
  • The framework's self-learning capability can enhance future diagnostic tools for infectious diseases.