"FWLICM-Deep Learning: Lung Lobe Segmentation for COVID-19 Detection"

What is it about?

Coronavirus (COVID-19) creates an extensive range of respiratory contagions, and it is a kind of ribonucleic acid (RNA) virus, which affects both animals and humans. Moreover, COVID-19 is a new disease, which produces contamination in upper respiration alterritory and lungs. The new COVID is a rapidly spreading pathogen globally, and it threatens billions of humans’ lives. However, it is significant to identify positive cases in order to avoid the spread of plague and to speedily treat infected patients. Hence, in this paper, the WSCA-based RMDL approach is devised for COVID-19 prediction by means of chest X-ray images. Moreover, Fuzzy Weighted Local Information C-Means (FWLICM) approach is devised in order to segment lung lobes. The developed FWLICM method is designed by modifying the Fuzzy Local Information C-Means (FLICM) technique. Additionally, random multimodel deep learning (RMDL) classifier is utilized for the COVID-19 prediction process. The new optimization approach, named water sine cosine algorithm (WSCA), is devised in order to obtain an effective prediction. The developed WSCA is newly designed by incorporating sine cosine algorithm (SCA) and water cycle algorithm (WCA). The developed WSCA-driven RMDL approach outperforms other COVID-19 prediction techniques with regard to accuracy, specificity, sensitivity, and dice score of 92.41%, 93.55%, 92.14%, and 90.02%.

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Why is it important?

The novel COVID disease is a severe deadly syndrome, which initiates from Wuhan territory, China, during December 2019 and spread worldwide The first case of COVID-19 is accounted in Wuhan, and it belongs to coronavirus (CoV) family, named as acute respiratory syndrome coronavirus 2 (SARS-CoV-2) prior it was known as COVID-19 through World Health Organization (WHO) in February 2020 The epidemic was confirmed by the Public Health Emergency of International Concern on 30 January 2020, and lastly, on March 11, 2020, WHO affirmed COVID-19 as a deadly disease. After the epidemic, the amount of day-to-day cases started to enlarge exponentially and attained 1.8 million cases and approximately 114,698 demises in worldwide by 12 April 2020.

Perspectives

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This paper presents and develop the WSCA-based RMDL approach for predicting COVID. Here, the chest X-ray image is considered an input for COVID prediction. This COVID performs the pre-processing process in order to eradicate the unrequited pixels and noises available in the input image. The ROI extraction and Laplacian filter are employed in order to eliminate the redundant pixels from the input image. The segmentation process is necessary in the prediction system for effective classification. Moreover, the FWLICM technique is devised for segmenting lung lobes from the pre-processed image. Here, the developed FWILCM is designed by the modification of the FLICM technique. Meanwhile, the RMDL classifier is employed for the prediction process of COVID. In addition, RMDL is trained through an introduced optimization approach, termed WSCA to achieve effective performance. However, the developed WSCA is newly developed by combining SCA and WCA. Moreover, the performance of the devised WSCA-enabled RMDL is evaluated by means of four metrics, like accuracy, sensitivity, specificity, and dice score. The introduced COVID prediction model achieves better performance with accuracy of 92.41%, sensitivity of 93.55%, specificity of 92.14%, and dice score of 92.02%. However, the classification of COVID-19 is not possible in the proposed method. In the future, hybrid deep learning classifier will be proposed for classifying the COVID-19 chest X-ray images.

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