Covid-19 classification by FGCNet with deep feature fusion from graph convolutional network and convolutional neural network

By Shui-hua Wang, V. Govindaraj, J. Górriz, Xin Zhang, Yudong Zhang. Information Fusion, 2020

302 citations recorded by the source corpus.

Highlights • We analysed over 320 COVID-19 images and 320 healthy control images.• We proposed an improved CNN to extract individual image-level features.• We proposed to use GCN to extract relation-aware representations.• We proposed a DFF technology to combine features from GCN and CNN.• The proposed FCGNet gives better performance than 15 state-of-the-art approaches.

Subfields: Artificial Intelligence, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/162846168d8e0ea31ed4652cae82271ddf38fa78

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Ahmed Bouridane
2. Ahmad Chaddad
3. Jihao Peng
4. Jian Xu

Citations in the corpus

• Survey of Explainable AI Techniques in Healthcare (2023, methodology): “[47] lung Grad-CAM CT It proposes a method of deep feature fusion.”