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Updated: Sep 5, 2025

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FWLICM-Deep Learning: Fuzzy Weighted Local Information C-Means Clustering-Based Lung Lobe Segmentation with Deep

R Rajeswari1, Veerraju Gampala2, Balajee Maram3

  • 1Department of Electronics and Communication Engineering, Rajalakshmi Institute of Technology, Chennai, India. rajimaniphd@gmail.com.

Journal of Digital Imaging
|July 5, 2022
PubMed
Summary

This study introduces a novel Water Sine Cosine Algorithm (WSCA)-driven Random Multimodel Deep Learning (RMDL) approach for accurate COVID-19 prediction using chest X-rays. The method achieves high accuracy, aiding in early detection and treatment of the respiratory illness.

Keywords:
COVIDFuzzy local information c-means clusteringRandom multimodel deep learningSine cosine algorithmWater cycle algorithm

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Virology

Background:

  • Coronavirus disease (COVID-19) is a global respiratory illness caused by an RNA virus, impacting both animals and humans.
  • Early and accurate identification of COVID-19 cases is crucial for disease containment and patient treatment.
  • Chest X-ray imaging is a key diagnostic tool for assessing respiratory infections like COVID-19.

Purpose of the Study:

  • To develop and evaluate a novel approach for predicting COVID-19 using chest X-ray images.
  • To enhance the accuracy and efficiency of COVID-19 detection through advanced machine learning techniques.

Main Methods:

  • A hybrid optimization algorithm, the Water Sine Cosine Algorithm (WSCA), was developed by combining the Sine Cosine Algorithm (SCA) and Water Cycle Algorithm (WCA).
  • A Fuzzy Weighted Local Information C-Means (FWLICM) method, a modification of FLICM, was used for lung lobe segmentation.
  • A Random Multimodel Deep Learning (RMDL) classifier, optimized by WSCA, was employed for COVID-19 prediction.

Main Results:

  • The WSCA-driven RMDL approach demonstrated superior performance in COVID-19 prediction compared to existing methods.
  • Achieved high performance metrics: 92.41% accuracy, 93.55% specificity, 92.14% sensitivity, and 90.02% dice score.
  • The FWLICM method effectively segmented lung lobes, contributing to the overall prediction accuracy.

Conclusions:

  • The proposed WSCA-driven RMDL method is a highly effective tool for COVID-19 prediction from chest X-ray images.
  • This approach offers a promising advancement in the early diagnosis and management of COVID-19.
  • The integration of novel optimization and deep learning techniques significantly improves diagnostic capabilities for respiratory diseases.