A hybrid deep learning framework for Covid-19 detection via 3D Chest CT Images
This work addresses COVID-19 diagnosis for medical imaging applications, but it is incremental as it builds on existing deep learning methods.
The paper tackled COVID-19 detection from 3D chest CT images by proposing CTNet, a hybrid deep learning framework combining CNN and transformer modules, which achieved advanced results on the COV19-CT-DB benchmark over state-of-the-art baselines.
In this paper, we present a hybrid deep learning framework named CTNet which combines convolutional neural network 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, over the state-of-the-art baseline approachproposed together with the dataset.