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Cardiac computed tomography (CT) scanning is an advanced cardiac imaging technique that utilizes CT technology, with or without intravenous (IV) contrast, to produce accurate cross-sectional virtual slices of specific areas of the heart, coronary circulation, and major blood vessels such as the aorta, pulmonary veins, and arteries. The computer processes these slices to generate three-dimensional images. Multidetector CT (MDCT) is a rapid form of CT scanning that captures multiple slices...
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Deep SVDD and Transfer Learning for COVID-19 Diagnosis Using CT Images.

Akram A Alhadad1, Reham R Mostafa2, Hazem M El-Bakry2

  • 1Computer Science Department, Ibb University, Ibb, Yemen.

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This study introduces a new method using transfer learning and deep support vector data description (DSVDD) for accurate COVID-19 diagnosis from CT scans. The approach effectively distinguishes COVID-19 from other lung conditions, aiding early detection and patient outcomes.

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Area of Science:

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

Background:

  • The COVID-19 pandemic has overwhelmed global health systems, necessitating rapid and accurate diagnostic tools.
  • Early diagnosis of COVID-19 is crucial for patient survival and limiting disease transmission.
  • Distinguishing COVID-19 from other respiratory illnesses like pneumonia using medical imaging is a significant challenge.

Purpose of the Study:

  • To develop and evaluate a novel deep learning approach for the automated diagnosis of COVID-19 using chest CT images.
  • To differentiate between COVID-19 positive cases, non-COVID-19 pneumonia, and healthy individuals.
  • To investigate the efficacy of transfer learning with deep support vector data description (DSVDD) for this diagnostic task.

Main Methods:

  • A novel approach combining transfer learning (VGG16 and ResNet50) with one-class deep support vector data description (DSVDD) was proposed.
  • Three distinct models were developed, each designed to classify a specific category as normal or anomalous.
  • Models were trained using CT image data from multiple sources, curated by an expert radiologist, employing end-to-end fusion and varying data split ratios (70%, 50%, 30%).

Main Results:

  • The proposed VGG16-based models achieved F1 scores of 0.8281, 0.9170, and 0.9294 for the different data splits.
  • The ResNet50-based models demonstrated strong performance with F1 scores of 0.9109, 0.9188, and 0.9333 across the data splits.
  • This represents the first known application of one-class DSVDD combined with transfer learning for diagnosing lung diseases, including COVID-19.

Conclusions:

  • The developed transfer learning and DSVDD approach shows high efficacy in distinguishing COVID-19 from other lung conditions on CT scans.
  • The method offers a promising tool for early and accurate COVID-19 diagnosis, potentially improving patient management and public health responses.
  • This study highlights the potential of advanced AI techniques in addressing critical challenges in medical diagnostics during global health crises.