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Coronavirus disease 2019 or COVID-19 is one of the biggest challenges which are being faced by mankind Researchers are continuously trying to discover a vaccine or medicine for this highly infectious disease but, proper success is not achieved to date Many countries are suffering from this disease and trying to find some solution that can prevent the dramatic spread of this virus Although the mortality rate is not very high, the highly infectious nature of this virus makes it a global threat RT-PCR test is the only means to confirm the presence of this virus to date Only precautionary measures like early screening, frequent hand wash, social distancing use of masks, and other protective equipment can prevent us from this virus Some researches show that the radiological images can be quite helpful for the early screening purpose because some features of the radiological images indicate the presence of the COVID-19 virus and therefore, it can serve as an effective screening tool Automated analysis of these radiological images can help the physicians and other domain experts to study and screen the suspected patients easily and reliably within the stipulated amount of time This method may not replace the traditional RT-PCR method for detection but, it can be helpful to filter the suspected patients from the rest of the community that can effectively reduce the spread in the of this virus A novel method is proposed in this work to segment the radiological images for the better explication of the COVID-19 radiological images The proposed method will be known as SuFMoFPA (Superpixel based Fuzzy Modified Flower Pollination Algorithm) The type 2 fuzzy clustering system is blended with this proposed approach to get the better-segmented outcome Obtained results are quite promising and outperforming some of the standard approaches which are encouraging for the practical uses of the proposed approach to screening the COVID-19 patients
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Expert_Systems_with_Applications
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SuFMoFPA: A superpixel and meta-heuristic based fuzzy image segmentation approach to explicate COVID-19 radiological images
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