A Hybrid and Fast Deep Learning Framework for COVID-19 Detection via 3D Chest CT Images

Shuang Liang, Weicun Zhang, Yu Gu; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, 2021, pp. 508-512

In this paper, we present a hybrid deep learning framework named CTNet which combines convolutional neural network (CNN) and transformer together for the detection of COVID-19 via 3D chest CT images. It consists of a CNN feature extractor module with SE attention to extract sufficient features from CT scans, together with a transformer model to model the discriminative features of the 3D CT scans. Compared to previous works, CTNet provides an effective and efficient method to perform COVID-19 diagnosis via 3D CT scans with data resampling strategy. Advanced results on a large and public benchmarks, COV19-CT-DB database, was achieved by the proposed CTNet with a macro F1 score of 88.21% on the validation set, which lead ten percentage over the state-of-the-art baseline approach proposed together with the dataset. Notably, the inference speed of the proposed framework is about ten times faster than that of the typical CNN frameworks which make it more promising in actual applications.