Deep Learning Based COVID-19 Detection Using Computed Tomography Images
DOI:
https://doi.org/10.22399/ijcesen.963Keywords:
COVID-19, deep neural network, CT images, deep learningAbstract
The infectious coronavirus disease (COVID-19), seen in Wuhan city of China in December 2019, led to a global pandemic, resulting in countless deaths. The healthcare sector has become extensively use of deep learning (DL), a method that is currently quite popular. The aim of this study is to identify the best and most successful deep learning model and optimizer approach combination for COVID-19 diagnosis. For this reason, several DL methods and optimizer techniques are tested on two comprehensive public data set to select the best DL model with optimizer technique. A variety of performance evaluation metrics, including f-score, precision, specificity, and accuracy, were used to assess the models' effectiveness. The experimental results show that the most suitable and effective architecture is DenseNet-201 in the network comparison, which achieved a 98% accuracy rate using the AdaGrad optimizer and 200 iterations.
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