Radiological image analysis using effective channel extension and fusion network based on COVID CT images

Abstract

Attention mechanisms demonstrate significant capabilities in improving the performance of COVID CT image classification networks. Existing strategies often employ global pooling to acquire feature vectors, overlooking local feature information. Complex attention modules are then applied to enhance network performance, inevitably leading to increased model complexity. To address these issues, this paper introduces an Effective Channel Expansion and Fusion (ECEF) module specifically designed to enhance the performance of medical COVID CT image classification. The module represents a simple yet effective convolutional neural network module. In the channel direction of the given feature map, the module performs segmentation, conducts global pooling operations separately on the segmented and overall feature maps to obtain corresponding attention vectors, and merges them to produce high-dimensional attention vectors. Channel attention feature vectors are derived along the two pooling operation directions and dynamically fused. The resulting attention vectors are multiplied by the input feature map for adaptive feature refinement. Through these operations, the ECEF module adeptly captures local features in COVID CT images, enhancing the classification performance of medical COVID CT images. As a lightweight and versatile module, ECEF seamlessly integrates into any CNN architecture with negligible overhead. It can undergo end-to-end training alongside basic CNNs. ResNet is selected as the backbone network for empirical studies conducted on COVID CT images, including the COVID-CT dataset. Experimental results indicate that, compared to current advanced channel attention mechanisms, the ECEF module exhibits superior performance in COVID CT image classification tasks. The successful application of the ECEF module establishes a foundation for its widespread use in areas such as COVID CT medical image segmentation and object detection.