Deep Transfer Learning Pipelines with Apache Spark and Keras TensorFlow combined with Logistic Regression to Detect COVID-19 in Chest CT Images

The SARS-CoV-2 (COVID-19) has propagated rapidly around the world and it became a global pandemic. It has generated a catastrophic effect on public health. It is crucial to discovered positive cases as early as possible to fastly treat touched patients. Chest CT is one of the methods that play a significant role in the diagnosis of 2019-nCoV acute respiratory disease. The implementation of advanced deep learning techniques combined with radiological imaging can be helpful for the precise detection of the novel coronavirus, and can also be assistive to surmount the difficult situation of the lack of medical skills and specialized physicians in remote regions. In this paper, we presented a Deep Transfer Learning Pipelines with Apache Spark and Keras TensorFlow combined with the Logistic Regresion algorithm for automatic COVID-19 detection in chest CT images, using Convolutional Neural Network (CNN) based models VGG16, VGG19, and Xception. Our model produced a classification accuracy of 85.64%, 84.25%, and 82.87%, respectively, for VGG16, VGG19, and Xception.