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Related Experiment Video

Updated: Sep 28, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
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Efficient COVID-19 CT Scan Image Segmentation by Automatic Clustering Algorithm.

Basu Dev Shivahare1, S K Gupta2

  • 1Department of Computer Science and Engineering, Dr. A.P.J Abdul Kalam Technical University, Lucknow, Uttar Pradesh, India.

Journal of Healthcare Engineering
|April 4, 2022
PubMed
Summary

This study introduces an improved whale optimization algorithm (IWOA) for automated COVID-19 detection from chest CT scans. The IWOA method achieved 97.49% accuracy in classifying COVID-19 using texture features from segmented CT images.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer-Aided Diagnosis

Background:

  • Rapid and accurate COVID-19 diagnosis is crucial due to RT-PCR test limitations, including time consumption and potential false negatives.
  • Automated analysis of chest CT scans offers a faster alternative for COVID-19 detection.
  • Existing diagnostic tools require improvement in speed and accuracy to aid clinical decision-making.

Purpose of the Study:

  • To develop and evaluate an automated system for segmenting and classifying COVID-19 and normal chest CT images.
  • To introduce an improved whale optimization algorithm (IWOA) for enhanced image segmentation.
  • To assess the efficacy of IWOA-based segmentation combined with machine learning for COVID-19 classification.

Main Methods:

  • Automated segmentation of chest CT images using the improved whale optimization algorithm (IWOA) and Otsu's method.
  • Extraction of 12 texture features using Discrete Wavelet Transform (DWT) and Principal Component Analysis (PCA) from segmented images.
  • Classification of COVID-19/non-COVID-19 cases using a random forest algorithm trained on extracted texture features.

Main Results:

  • The IWOA demonstrated superior performance in image segmentation compared to WOA, SSA, and SCA, achieving better evaluation metrics and segmentation masks.
  • The random forest classifier, utilizing DWT-PCA texture features from IWOA-segmented images, achieved a high classification accuracy of 97.49%.
  • The proposed method significantly improved segmentation and classification efficiency for COVID-19 detection in chest CT scans.

Conclusions:

  • The IWOA is an effective optimization algorithm for medical image segmentation, particularly for chest CT scans in COVID-19 detection.
  • Combining IWOA-based segmentation with DWT-PCA features and random forest classification provides a highly accurate automated tool for diagnosing COVID-19.
  • This automated approach can expedite COVID-19 diagnosis, potentially improving patient outcomes by enabling earlier treatment initiation.