ABSTRACT
COVID-19 pandemic caused a vast impact worldwide the imbalance between the number of tools for COVID-19 detection and the demand for COVID-19 tests from citizens has overwhelmed the government. To overcome this problem, artificial intelligence is utilized, specifically in the deep learning field. In this paper, we propose FJCovNet, a new deep learning model based on DenseNet121. FJCovNet managed to get an accuracy of 98.14%, surpassing Xception with an accuracy of 84,24%, VGG19 with an accuracy of 95.25%, and ResNet50 with accuracy of 91.53%. FJCovNet also managed to get less training time with 612 seconds, lesser than VGG19 with 808 seconds and ResNet50 with 809 seconds, and only slightly more than Xception with 609 seconds. © 2021 IEEE.
ABSTRACT
COVID-19 pandemic caused vast impact worldwide. Many efforts have been made to tackle the pandemic, including in the deep learning community. In this research, a modification of deep neural network based on Xception model is proposed. The model is used for COVID-19 detection based on the chest X-ray images. The proposed model implements two stacks of two dense layers and batch normalization. The layers addition is used to avoid overfitting of the proposed model. The performance of the proposed model is compared to Resnet50, InceptionV3 and Xception. The experiment result shows that the proposed model has better performance than the other models used in the research. However, its computational time is higher than the other models used in the research. © 2020 IEEE.