Abstract: A novel coronavirus disease (COVID-19) has been a severe world threat to humans since December 2020. The virus mainly affects the human respiratory system, making breathing difficult. Early detection and Diagnosis are essential to controlling the disease. Radiological imaging, like Computed Tomography (CT) scans, produces clear, high-quality chest images and helps quickly diagnoses lung abnormalities. The recent advancements in Artificial intelligence enable accurate and fast detection of COVID-19 symptoms on chest CT images. This paper presents COVIDnet, an improved and efficient deep learning Model for COVID-19 diagnosis on chest CT images. We developed a chest CT dataset from 220 CT studies from Tamil Nadu, India, to evaluate the proposed model. The final dataset contains 5191 CT images (3820 COVID-infected and 1371 normal CT images). The proposed COVIDnet model aims to produce accurate diagnostics for classifying these two classes. Our experimental result shows that COVIDnet achieved a superior accuracy of 98.98% when compared with three contemporary deep learning models.

Briskline Kiruba S, Murugan D and Petchiammal A, “COVIDnet: An Efficient Deep Learning Model for COVID-19 Diagnosis on Chest CT Images” International Journal of Advanced Computer Science and Applications(IJACSA), 13(11), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0131196

@article{S2022,
title = {COVIDnet: An Efficient Deep Learning Model for COVID-19 Diagnosis on Chest CT Images},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0131196},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0131196},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {11},
author = {Briskline Kiruba S and Murugan D and Petchiammal A}
}