Unify Language and Vision: An Efficient COVID-19 Tomography Image Classification Approach

Abstract

An accurate and efficient image classification algorithm used in the COVID-19 detection for lung tomography can be of great help for doctors working in places without advance equipments. The machine with high accuracy COVID-19 classification model can relief the burden by making testing and checking thousands of people’s tomography images easy for a specific region which suffers from the COVID-19 outbreak incidents. By encoding image pixels and meta-data using the pre-trained language models of Bidirectional Encoder Representations from Transformers, then connect to a fully connected layer, the classification model outperforms the ResNet model and the DenseNet image classification model, and achieved accuracy of 99.51% ∼ 100.00% on the COVID-19 tomography image test set.