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COVID-19 Detection from Computed Tomography Images Using Slice Processing Techniques and a Modified Xception

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Science

Background:

  • Accurate COVID-19 diagnosis is crucial for patient management and public health.
  • Computed Tomography (CT) imaging is a key tool for COVID-19 detection.
  • Existing diagnostic models require enhancement for improved accuracy and efficiency.

Purpose of the Study:

  • To develop an enhanced computational method for COVID-19 detection using CT images.
  • To improve the accuracy of COVID-19 diagnosis by reducing model misclassifications.
  • To leverage transfer learning for efficient and effective analysis of medical imaging data.

Main Methods:

  • A lean transfer learning model based on the Xception architecture was employed.
  • Image preprocessing involved removing extreme slices and manual lung area cropping.
  • CT scans were resized to 224x224 and processed using the modified Xception model.
  • The model was trained and validated on a large, rigorously annotated CT image database (>5000 patients).

Main Results:

  • The enhanced method demonstrated improved performance compared to previous solutions and baseline models.
  • The proposed approach achieved performance comparable to top-performing methods on the COV19-CT database.
  • Validation on unseen images confirmed the model's effectiveness on the test partition.

Conclusions:

  • The developed transfer learning model offers a promising approach for accurate COVID-19 detection from CT scans.
  • The image processing techniques effectively reduced misclassifications and highlighted relevant lung features.
  • Future research should focus on scalability, adaptability, and integration with advanced image analysis techniques.