Application of Deep Learning for Early Detection of COVID-19 Using CT-Scan Images

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.

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