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SUFMACS: A machine learning-based robust image segmentation framework for COVID-19 radiological image interpretation.

Shouvik Chakraborty1, Kalyani Mali1

  • 1Department of Computer Science and Engineering, University of Kalyani, India.

Expert Systems with Applications
|April 26, 2021
PubMed
Summary

Early COVID-19 diagnosis is crucial. A new unsupervised machine learning method, SUFMACS, uses radiological images for efficient COVID-19 screening, addressing the lack of annotated data.

Keywords:
COVID-19ClusteringImage segmentationMachine learningRadiological image interpretationSUFMACS

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computational Biology

Background:

  • COVID-19 pandemic highlights the need for rapid and reliable diagnostic tools.
  • While RT-PCR is a gold standard, radiological imaging offers a contactless screening alternative.
  • A significant challenge in developing AI for medical imaging is the scarcity of annotated ground truth data.

Purpose of the Study:

  • To propose a novel unsupervised machine learning method, SUFMACS, for interpreting and segmenting COVID-19 radiological images.
  • To address the challenge of limited annotated data in medical image analysis for COVID-19.
  • To evaluate the effectiveness of SUFMACS on both CT scan and X-ray images.

Main Methods:

  • Developed SUFMACS (SUperpixel based Fuzzy Memetic Advanced Cuckoo Search), an unsupervised machine learning approach.
  • Adapted superpixel techniques to reduce spatial information in radiological images.
  • Modified the cuckoo search algorithm, incorporating Luus-Jaakola and McCulloch's methods to optimize a fuzzy objective function.

Main Results:

  • The SUFMACS method demonstrated promising qualitative and quantitative results in interpreting and segmenting COVID-19 radiological images.
  • The approach effectively utilizes superpixel advantages within the fuzzy objective function.
  • Both CT scan and X-ray images were analyzed, showing the method's versatility.

Conclusions:

  • The proposed SUFMACS method offers an efficient and applicable solution for COVID-19 screening using radiological images.
  • Unsupervised learning presents a viable pathway for medical image analysis, particularly when annotated data is scarce.
  • The study validates the real-world applicability and efficiency of the SUFMACS approach for COVID-19 detection.