Abstract: The imaging modalities of chest X-rays and computed tomography (CT) are commonly utilized to quickly and accurately diagnose COVID-19. Due to time and human error, it is exceedingly difficult to manually identify the infection using radio imaging. COVID-19 identification is being mechanized and improved with the use of artificial intelligence (AI) tools that have already showed promise. This study employs the following methodology: The chest footage was pre-processed by setting equalizing the histogram, sharpening it, and so on. The transformed chest images are then retrieved through shallow and high-level feature mapping over the backbone network. To further improve the classification performance of the convolutional neural network, the model uses self-attained mechanism through feature maps. Numerous simulations show that CT image classification and augmentation may be accomplished with higher efficiency and flexibility using the Inception-Resnet convolutional neural network than with traditional segmentation methods. The experiment illustrates the association between model accuracy, model loss, and epoch. Inception-statistical Resnet's measurement results are 98%, 91%, 91%.

Mohammed Sidheeque, P. Sumathy and Abdul Gafur. M, “Deep Learning and Classification Algorithms for COVID-19 Detection” International Journal of Advanced Computer Science and Applications(IJACSA), 13(9), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130940

@article{Sidheeque2022,
title = {Deep Learning and Classification Algorithms for COVID-19 Detection},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130940},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130940},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {9},
author = {Mohammed Sidheeque and P. Sumathy and Abdul Gafur. M}
}