@Article{cmes.2023.025804,
AUTHOR = {Jiaji Wang, Shuwen Chen, Yu Cao, Huisheng Zhu, Dimas Lima},
TITLE = {COVID-19 Detection Based on 6-Layered Explainable Customized Convolutional Neural Network},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {136},
YEAR = {2023},
NUMBER = {3},
PAGES = {2595--2616},
URL = {http://www.techscience.com/CMES/v136n3/51834},
ISSN = {1526-1506},
ABSTRACT = {This paper presents a 6-layer customized convolutional neural network model (6L-CNN) to rapidly screen out patients with COVID-19 infection in chest CT images. This model can effectively detect whether the target CT image contains images of pneumonia lesions. In this method, 6L-CNN was trained as a binary classifier using the dataset containing CT images of the lung with and without pneumonia as a sample. The results show that the model improves the accuracy of screening out COVID-19 patients. Compared to other methods, the performance is better. In addition, the method can be extended to other similar clinical conditions.},
DOI = {10.32604/cmes.2023.025804}
}