Computer Aided Diagnosis for COVID-19 in CT Images Utilizing Transfer Learning and Attention Mechanism

Various and intricate varieties of lung disease have made it challenging for computer aided diagnosis to appropriately segment lung lesions utilizing computed tomography (CT) images. This study integrates transfer learning with the attention mechanism to construct a deep learning model that can automatically detect new coronary pneumonia on lung CT images. In this study, using VGG16 pre-trained by ImageNet as the encoder, the decoder was established utilizing the U-Net structure. The attention module is incorporated during each concatenate procedure, permitting the model to concentrate on the critical information and identify the crucial components efficiently. The public COVID-19-CT-Seg-Benchmark dataset was utilized for experiments, and the highest scores for Dice, F 1 , and Accuracy were 0.9071, 0.907 6, and 0.996 5, respectively. The generalization performance was assessed concurrently, with performance metrics including Dice, F 1 , and Accuracy over 0.8. The experimental findings indicate the feasibility of the segmentation network proposed in this study.