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

https://doi.org/10.1016/j.eswa.2021.115069Get rights and content

Highlights

  • A novel superpixel and based fuzzy image segmentation method is proposed.

  • This method is useful in the early screening of the COVID-19 infected patients.

  • The original cuckoo search approach is modified and updated in two ways.

  • A fuzzy modified objective function is proposed.

  • The proposed method can be adapted for the real-life applications.

Abstract

The absence of dedicated vaccines or drugs makes the COVID-19 a global pandemic, and early diagnosis can be an effective prevention mechanism. RT-PCR test is considered as one of the gold standards worldwide to confirm the presence of COVID-19 infection reliably. Radiological images can also be used for the same purpose to some extent. Easy and no contact acquisition of the radiological images makes it a suitable alternative and this work can help to locate and interpret some prominent features for the screening purpose. One major challenge of this domain is the absence of appropriately annotated ground truth data. Motivated from this, a novel unsupervised machine learning-based method called SUFMACS (SUperpixel based Fuzzy Memetic Advanced Cuckoo Search) is proposed to efficiently interpret and segment the COVID-19 radiological images. This approach adapts the superpixel approach to reduce a large amount of spatial information. The original cuckoo search approach is modified and the Luus-Jaakola heuristic method is incorporated with McCulloch’s approach. This modified cuckoo search approach is used to optimize the fuzzy modified objective function. This objective function exploits the advantages of the superpixel. Both CT scan and X-ray images are investigated in detail. Both qualitative and quantitative outcomes are quite promising and prove the efficiency and the real-life applicability of the proposed approach.

Keywords

COVID-19
Image segmentation
Radiological image interpretation
Machine learning
Clustering
SUFMACS

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