@Article{jai.2022.032974,
AUTHOR = {Tahani Maazi Almutairi, Mohamed Maher Ben Ismail, Ouiem Bchir},
TITLE = {X-ray Based COVID-19 Classification Using Lightweight EfficientNet},
JOURNAL = {Journal on Artificial Intelligence},
VOLUME = {4},
YEAR = {2022},
NUMBER = {3},
PAGES = {167--187},
URL = {http://www.techscience.com/jai/v4n3/50701},
ISSN = {2579-003X},
ABSTRACT = {The world has been suffering from the Coronavirus (COVID-19) pandemic since its appearance in late 2019. COVID-19 spread has led to a drastic increase of the number of infected people and deaths worldwide. Imminent and accurate diagnosis of positive cases emerged as a natural alternative to reduce the number of serious infections and limit the spread of the disease. In this paper, we proposed an X-ray based COVID-19 classification system that aims at diagnosing positive COVID-19 cases. Specifically, we adapted lightweight versions of EfficientNet as backbone of the proposed recognition system. Particularly, lightweight EfficientNet networks were used to build classification models able to discriminate between positive and negative COVID-19 cases using chest X-ray images. The proposed models ensure a trade-off between scaling down the architecture of the deep network to reduce the computational cost and optimizing the classification performance. In the experiments, a public dataset containing 7,345 chest X-ray images was used to train, validate and test the proposed models for binary and multi-class classification problems, respectively. The obtained results showed the EfficientNet-elite-B9-V2, which is the lightest proposed model yielded an accuracy of 96%. On the other hand, EfficientNet-lite-B0 overtook the other models, and achieved an accuracy of 99%.},
DOI = {10.32604/jai.2022.032974}
}