SUFEMO: A superpixel based fuzzy image segmentation method for COVID-19 radiological image elucidation.

COVID-19 causes an ongoing worldwide pandemic situation. The non-discovery of specialized drugs and/or any other kind of medicines makes the situation worse. Early diagnosis of this disease will be certainly helpful to start the treatment early and also to bring down the dire spread of this highly infectious virus.

A radiological image analysis framework for early screening of the COVID-19 infection: A computer vision-based approach.

Due to the absence of any specialized drugs, the novel coronavirus disease 2019 or COVID-19 is one of the biggest threats to mankind Although the RT-PCR test is the gold standard to confirm the presence of this virus, some radiological investigations find some important features from the CT scans of the chest region, which are helpful to identify the suspected COVID-19 patients. This article proposes a novel fuzzy superpixel-based unsupervised clustering approach that can be useful to automatically process the CT scan images without any manual annotation and helpful in the easy interpretation. The proposed approach is based on artificial cell swarm optimization and will be known as the SUFACSO (SUperpixel based Fuzzy Artificial Cell Swarm Optimization) and implemented in the Matlab environment.

SuFMoFPA: A superpixel and meta-heuristic based fuzzy image segmentation approach to explicate COVID-19 radiological images.

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.

An Unsupervised Fuzzy Clustering Approach for Early Screening of COVID-19 From Radiological Images.

A global pandemic scenario is witnessed worldwide owing to the menace of the rapid outbreak of the deadly COVID-19 virus. To save mankind from this apocalyptic onslaught, it is essential to curb the fast spreading of this dreadful virus. Moreover, the absence of specialized drugs has made the scenario even more badly and thus an early-stage adoption of necessary precautionary measures would provide requisite supportive treatment for its prevention.

A morphology-based radiological image segmentation approach for efficient screening of COVID-19.

Computer-aided radiological image interpretation systems can be helpful to reshape the overall workflow of the COVID-19 diagnosis process. This article describes an unsupervised CT scan image segmentation approach. This approach begins by performing a morphological reconstruction operation that is useful to remove the effect of the external disturbances on the infected regions and to locate different regions of interest precisely.

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

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.

Fuzzy Electromagnetism Optimization (FEMO) and its application in biomedical image segmentation.

In this work, a new unsupervised classification approach is proposed for the biomedical image segmentation. The proposed method will be known as Fuzzy Electromagnetism Optimization (FEMO). As the name suggests, the proposed approach is based on the electromagnetism-like optimization (EMO) method.

Modified cuckoo search algorithm in microscopic image segmentation of hippocampus.

Microscopic image analysis is one of the challenging tasks due to the presence of weak correlation and different segments of interest that may lead to ambiguity. It is also valuable in foremost meadows of technology and medicine. Identification and counting of cells play a vital role in features extraction to diagnose particular diseases precisely.