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Per-COVID-19: A Benchmark Dataset for COVID-19 Percentage Estimation from CT-Scans.

Fares Bougourzi1, Cosimo Distante1, Abdelkrim Ouafi2

  • 1Institute of Applied Sciences and Intelligent Systems, National Research Council of Italy, 73100 Lecce, Italy.

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Summary

This study introduces a COVID-19 percentage estimation dataset from CT scans. Convolutional Neural Network models accurately estimate infection percentage, aiding critical care resource allocation and patient monitoring.

Keywords:
COVID-19CT-scansconvolutional neural networkdataset generationdeep learning

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology

Background:

  • Accurate COVID-19 infection recognition is crucial for pandemic management.
  • Computed Tomography (CT) scans offer insights into disease evolution and severity beyond initial diagnosis.
  • Estimating COVID-19 infection percentage from CT scans can optimize intensive care unit (ICU) bed allocation and patient management protocols.

Purpose of the Study:

  • To introduce a novel dataset for COVID-19 percentage estimation from CT scans.
  • To evaluate the performance of various Convolutional Neural Network (CNN) architectures for this estimation task.
  • To compare different loss functions and pre-training strategies for optimizing CNN performance.

Main Methods:

  • Development of a COVID-19 percentage estimation dataset, with labels provided by expert radiologists.
  • Evaluation of three CNN architectures: ResneXt-50, Densenet-161, and Inception-v3.
  • Comparison of Mean Squared Error (MSE) and Dynamic Huber loss functions.
  • Investigation of two pre-training scenarios: ImageNet and X-ray data pre-trained models.

Main Results:

  • The Inception-v3 architecture, utilizing Dynamic Huber loss and X-ray pre-trained models, demonstrated superior performance.
  • Slice-level results showed a Pearson Correlation coefficient (PC) of 0.9365, Mean Absolute Error (MAE) of 5.10, and Root Mean Square Error (RMSE) of 9.25.
  • Subject-level results achieved a PC of 0.9603, MAE of 4.01, and RMSE of 6.79, indicating high accuracy in estimating overall infection percentage per patient.

Conclusions:

  • CNN architectures provide an accurate and efficient method for estimating COVID-19 infection percentage from CT scans.
  • The developed dataset and evaluated models can significantly aid in monitoring patient disease progression.
  • This approach supports clinical decision-making by providing quantitative insights into infection severity and evolution.