| |
| Community-acquired pneumonia (CAP) poses a global public health challenge, and in the current environment of the pneumonia pandemic, timely and accurate diagnosis of different types of pneumonia is particularly crucial. Computed Tomography (CT) is an effective means of diagnosing pneumonia, and the use of artificial intelligence (AI) for diagnostic assistance can enhance clinical diagnostic efficiency. Therefore, this paper introduces a 3D multitask deep learning model called TransFuseNet to achieve real-time and accurate segmentation and classification of CAP.Specifically, the proposed network consists of two sub-networks: a 3D scSEU-Net sub-network for pneumonia lesion segmentation and a classification sub-network based on a fully convolutional Transformer. Both sub-networks share the same encoder, where the segmentation branch captures local features and spatial relationships, while the classification branch performs long-range modeling to capture global context information. Simultaneously, a loss function is introduced to enhance the interaction between the two sub-networks, balancing the importance of the two tasks.The retrospective dataset includes 180 patients who underwent thin-slice chest CT scans at a medical center in China. Numerous experiments demonstrate that the model achieved AUC: 0.989, DSC: 0.723, average accuracy: 0.927, precision: 0.889, sensitivity: 0.866, and specificity: 0.835 on the test set. The model shows no significant difference in pneumonia detection accuracy compared to radiologists. |
|
| Keywords:Computer application technology; Multi-task learning; Segmentation and classification of pneumonia; 3D network |
|
