GOLF-Net: Global and local association fusion network for COVID-19 lung infection segmentation

The global spread of the Corona Virus Disease 2019 (COVID-19) has caused significant health hazards, leading researchers to explore new methods for detecting lung infections that can supplement molecular diagnosis. Computer tomography (CT) has emerged as a promising tool, although accurately segmenting infected areas in COVID-19 CT scans, especially given the limited available data, remains a challenge for deep learning models. To address this issue, we propose a novel segmentation network, the GlObal and Local association Fusion Network (GOLF-Net), that combines global and local features from Convolutional Neural Networks and Transformers, respectively. Our network leverages attention mechanisms to enhance the correlation and representation of local features, improving the accuracy of infected area segmentation. Additionally, we implement transfer learning to pretrain our network parameters, providing a robust solution to the issue of limited COVID-19 CT data. Our experimental results demonstrate that the segmentation performance of our network exceeds that of most existing models, with a Dice coefficient of 95.09% and an IoU of 92.58%. & COPY; 2014 Hosting by Elsevier B.V. All rights reserved.

Automated summary

The global spread of COVID-19 has posed significant health risks, and researchers are seeking new methods to detect lung infections. Utilizing CT scans and deep learning models to accurately segment infected areas remains a challenge. To address this, a novel segmentation network called GOLF-Net is proposed, combining global and local features to enhance the accuracy of infected area segmentation. Transfer learning is implemented to overcome limited CT data. Results show that GOLF-Net outperforms existing models with a Dice coefficient of 95.09% and an IoU of 92.58%.