Fine-Tuning ResNet-152V2 for COVID-19 Recognition in Chest Computed Tomography Images

The epidemic Covid-19 has extended to majority of nations. This pandemic is due to a contagious condition ‘SARS-CoV-2’, was identified by the the International Health association. In order to diagnosis this virus from 2D chest computed tomography (CT) images, we applied three different transfer learning algorithms: $VGG-19, ResNet-152V2$ and a Fine-Tuned version of $ResNet-152V2$. The different transfer learning models are used on three hundred and four exams where 74 are normal cases, 60 are community-acquired pneumonia (CAP) cases and 169 were confirmed corona-virus cases. The best accuracy value is reached by the fine-tuned $ResNet-152v2$ by 75% against 70% for the basic $ResNet-152v2$ and 66% for the $VGG-19$.