COVID-19 identification in chest X-ray images using intelligent multi-level classification scenario

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dc.contributor.authorBabukarthik, R.G.
dc.contributor.authorChandramohan, Dhasarathan
dc.contributor.authorTripathi, Diwakar
dc.contributor.authorKumar, Manish
dc.contributor.authorSambasivam, G.
dc.date.accessioned2023-07-12T15:51:44Z
dc.date.available2023-07-12T15:51:44Z
dc.date.issued2022
dc.description.abstractCOVID-19 is an evolving respiratory transmittable disease, and it holds all daily activity worldwide as a global pandemic. It appeared in the city of Wuhan (China) in November 2019 and slowly started spreading to the rest of the world. The number of cases keeps increasing drastically, leading to a shortage of medical resources and testing kids worldwide. As the physicians facing this problem, several scientists and specialists in Artificial Intelligent (AI) are rendering their support to healthcare professionals in the early detection of COVID-19 using chest X-ray image samples to determine the level of severity at a low cost. This paper proposed Genetic Deep Learning Convolutional Neural Network (GDCNN) architecture that includes Huddle Particle Swarm Optimization as an alternative to Gradient descent. Huddle PSO performs better when clubbed with GDCNN architecture. Based on publicly available datasets, trained chest X-ray images are used to predict and identify various pneumonia diseases. The proposed model performed better with an accuracy of 97.23%, a sensitivity of 98.62%, specificity of 97.0%, and precision of 93.0%. The proposed model act as a tool for earlier detection of COVID-19. In the future, we plan to apply the proposed model for the larger dataset and to predict various lung diseases.en_US
dc.identifier.citationBabukarthik, R. G., Chandramohan, D., Tripathi, D., Kumar, M., & Sambasivam, G. (2022). COVID-19 identification in chest X-ray images using intelligent multi-level classification scenario. Computers and Electrical Engineering, 104, 108405. https://doi.org/10.1016/j.compeleceng.2022.108405en_US
dc.identifier.urihttps://doi.org/10.1016/j.compeleceng.2022.108405
dc.identifier.urihttps://nru.uncst.go.ug/handle/123456789/9062
dc.language.isoenen_US
dc.publisherComputers and Electrical Engineeringen_US
dc.subjectGenetic deep learning convolutional neural networken_US
dc.subjectHuddle particle swarmen_US
dc.subjectOptimizationen_US
dc.subjectCOVID-19en_US
dc.subjectPneumoniaen_US
dc.subjectGenetic algorithmen_US
dc.titleCOVID-19 identification in chest X-ray images using intelligent multi-level classification scenarioen_US
dc.typeArticleen_US
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