Related papers: A hybrid deep learning framework for Covid-19 detection via 3D Chest CT Images

A hybrid deep learning framework for Covid-19 detection via 3D Chest CT Images

URL: http://arxiv.org/abs/2107.03904v2
Date: Fri, 9 Jul 2021 11:16:14 GMT
Title: A hybrid deep learning framework for Covid-19 detection via 3D Chest CT Images
Authors: Shuang Liang
Abstract summary: 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.
Score: 5.3708513698154015
License: http://creativecommons.org/licenses/by/4.0/
Abstract: 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.

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