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Related Experiment Video

Updated: Aug 23, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
08:05

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A robust semantic lung segmentation study for CNN-based COVID-19 diagnosis.

Muhammet Fatih Aslan1

  • 1Electrical and Electronics Engineering, Karamanoglu Mehmetbey University, Karaman, Turkey.

Chemometrics and Intelligent Laboratory Systems : an International Journal Sponsored by the Chemometrics Society
|October 31, 2022
PubMed
Summary

This study developed a deep learning system for diagnosing COVID-19 using chest X-rays. The AI model achieved 99.8% accuracy in classifying COVID-19, normal, and viral pneumonia cases.

Keywords:
AlexNetCOVID-19Convolutional neural networksDeepLabV3+Semantic segmentationSupport vector machine

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • Accurate and timely diagnosis of COVID-19 is crucial for patient management and public health.
  • Chest X-ray (CXR) imaging is a widely available tool for respiratory illness assessment.
  • Deep learning offers potential for automated analysis of medical images.

Purpose of the Study:

  • To develop and evaluate a deep learning system for diagnosing COVID-19 using CXR images.
  • To segment lung regions accurately from CXR scans for further analysis.
  • To classify CXR images into three categories: Normal, Viral Pneumonia, and COVID-19.

Main Methods:

  • Utilized the COVID-19 Chest X-Ray Dataset for semantic lung segmentation with DeepLabV3+.
  • Applied image preprocessing techniques to enhance CXR images from the COVID-19 Radiography Database.
  • Employed a modified AlexNet (mAlexNet) for feature extraction and Support Vector Machine (SVM) for classification.

Main Results:

  • Successfully segmented lung regions from CXR images.
  • Achieved a 99.8% classification success rate for distinguishing between Normal, Viral Pneumonia, and COVID-19 cases.
  • Demonstrated superior performance compared to existing state-of-the-art methods.

Conclusions:

  • The proposed deep learning approach provides a highly accurate method for COVID-19 diagnosis using CXR.
  • Automated analysis of CXR images can significantly aid in the rapid identification of COVID-19.
  • The system shows promise for integration into clinical diagnostic workflows.