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Novel Adaptive Histogram Binning-Based Lesion Segmentation for Discerning Severity in COVID-19 Chest CT Scan Images

Author

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  • S. Nivetha

    (Department of Computer Science, Periyar University, Salem, India)

  • H. Hannah Inbarani

    (Department of Computer Science, Periyar University, Salem, India)

Abstract

Coronavirus sickness (COVID-19) recently adversely disrupted the medical care system and the entire economy. Doctors, researchers, and specialists are working on new-fangled methods to detect COVID-19 relatively efficiently, such as constructing computerized COVID-19 detection systems. Medical imaging, such as Computed Tomography (CT), has a lot of opportunity as a solution to RT-PCR approaches for quantitative assessment and disease monitoring. COVID-19 diagnosis based on CT images can provide speedy and accurate results. A quantitative criterion for diagnosis is provided by an automated segmentation method of infection areas in the lungs. As an outcome, automatic image segmentation is in high demand as a clinical decision aid tool. To detect COVID-19, Computed Tomography images might be employed instead of the time-consuming RT-PCR assay. In this research, a unique technique is provided for segmenting infection areas in the lungs using CT scan images from COVID-19 patients. “Ground Glass Opacity (GGO)” regions were detected using Novel Adaptive Histogram Binning Based Lesion Segmentation (NAHBLS) method. Many metrics were also employed to evaluate the proposed method, including “Sorensen–Dice similarity”, “Sensitivity”, “Specificity”, “Precision”, and “Accuracy” measures. Experiments have shown that the proposed method can effectively separate the lung infections with good accuracy. The results show that the proposed Novel Adaptive Histogram Binning Based Lesion Segmentation based on automatic approach is effective at segmenting the lesion region of the image and calculated the Infection Rate (IR) over the lung region in Computed Tomography scan.

Suggested Citation

  • S. Nivetha & H. Hannah Inbarani, 2023. "Novel Adaptive Histogram Binning-Based Lesion Segmentation for Discerning Severity in COVID-19 Chest CT Scan Images," International Journal of Sociotechnology and Knowledge Development (IJSKD), IGI Global, vol. 15(1), pages 1-35, January.
  • Handle: RePEc:igg:jskd00:v:15:y:2023:i:1:p:1-35
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    References listed on IDEAS

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    1. S. Udhaya Kumar & Ahmad Taher Azar & H. Hannah Inbarani & O. Joseph Liyaskar & Khaled Mohamad Almustafa, 2019. "Weighted Rough Set Theory for Fetal Heart Rate Classification," International Journal of Sociotechnology and Knowledge Development (IJSKD), IGI Global, vol. 11(4), pages 1-19, October.
    2. D. V. N. Ananth & Lagudu Venkata Suresh Kumar & Tulasichandra Sekhar Gorripotu & Ahmad Taher Azar, 2021. "Design of a Fuzzy Logic Controller for Short-Term Load Forecasting With Randomly Varying Load," International Journal of Sociotechnology and Knowledge Development (IJSKD), IGI Global, vol. 13(4), pages 32-49, October.
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    Cited by:

    1. S. Nivetha & H. Hannah Inbarani, 2023. "Novel Hybrid Genetic Arithmetic Optimization for Feature Selection and Classification of Pulmonary Disease Images," International Journal of Sociotechnology and Knowledge Development (IJSKD), IGI Global, vol. 15(1), pages 1-58, January.

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    1. S. Nivetha & H. Hannah Inbarani, 2023. "Novel Hybrid Genetic Arithmetic Optimization for Feature Selection and Classification of Pulmonary Disease Images," International Journal of Sociotechnology and Knowledge Development (IJSKD), IGI Global, vol. 15(1), pages 1-58, January.

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