COVID-19 identification in chest X-ray images using intelligent multi-level classification scenario
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Date
2022
Journal Title
Journal ISSN
Volume Title
Publisher
Computers and Electrical Engineering
Abstract
COVID-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.
Description
Keywords
Genetic deep learning convolutional neural network, Huddle particle swarm, Optimization, COVID-19, Pneumonia, Genetic algorithm
Citation
Babukarthik, 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.2022.108405