Abstract
AbstractCOVID-19 is quickly gaining popularity across the globe. By April 14, 2020, 128,000 individuals had been killed by COVID-19, and 1.99 million incidents had been recorded in 210 countries and regions, totaling 219.747 cases. The rapid spread of the virus throughout the globe has resulted in a severe shortage of medical test kits in many parts of the world, particularly in Africa. A chest X-ray may prove to be a more successful screening method in certain situations than thermal screening of the whole body, due to the fact that the respiratory system is the most susceptible area in a human’s body to infection. Lung segmentation is the initial stage in identifying diseases using a chest x-ray picture. We describe a method for segmenting the lung region from CXR images that is based on the Euler number thresholding approach, i. When compared to current state-of-the-art methods, the suggested method demonstrates superior accuracy and performance.KeywordsCOVID-19Chest X-rayResnet-50Euler numberImageNetConfusion matrixROC
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