ECF-Net: Enhanced, Channel-Based, Multi-Scale Feature Fusion Network for COVID-19 Image Segmentation

Accurate segmentation of COVID-19 lesion regions in lung CT images aids physicians in analyzing and diagnosing patients’ conditions. However, the varying morphology and blurred contours of these regions make this task complex and challenging. Existing methods utilizing Transformer architecture lack...

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Bibliographic Details
Published in:Electronics (Basel) 2024-09, Vol.13 (17), p.3501
Main Authors: Ji, Zhengjie, Zhou, Junhao, Wei, Linjing, Bao, Shudi, Chen, Meng, Yuan, Hongxing, Zheng, Jianjun
Format: Article
Language:English
Subjects:
Accuracy
Computed tomography
Computer vision
Coronaviruses
COVID-19
Deep learning
Image enhancement
Image segmentation
Lesions
Medical imaging
Medical research
Neural networks
Research methodology
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Summary:Accurate segmentation of COVID-19 lesion regions in lung CT images aids physicians in analyzing and diagnosing patients’ conditions. However, the varying morphology and blurred contours of these regions make this task complex and challenging. Existing methods utilizing Transformer architecture lack attention to local features, leading to the loss of detailed information in tiny lesion regions. To address these issues, we propose a multi-scale feature fusion network, ECF-Net, based on channel enhancement. Specifically, we leverage the learning capabilities of both CNN and Transformer architectures to design parallel channel extraction blocks in three different ways, effectively capturing diverse lesion features. Additionally, to minimize irrelevant information in the high-dimensional feature space and focus the network on useful and critical information, we develop adaptive feature generation blocks. Lastly, a bidirectional pyramid-structured feature fusion approach is introduced to integrate features at different levels, enhancing the diversity of feature representations and improving segmentation accuracy for lesions of various scales. The proposed method is tested on four COVID-19 datasets, demonstrating mIoU values of 84.36%, 87.15%, 83.73%, and 75.58%, respectively, outperforming several current state-of-the-art methods and exhibiting excellent segmentation performance. These findings provide robust technical support for medical image segmentation in clinical practice.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics13173501