Faculty of Computer Science and Information Technology, Jazan University, Saudi Arabia.
Department of Computer Science and Engineering, Gautam Buddha University, Greater Noida, India.
Biomed Res Int. 2022 Aug 21;2022:5061112. doi: 10.1155/2022/5061112. eCollection 2022.
Weighted MR images of 421 patients with nasopharyngeal cancer were obtained at the head and neck level, and the tumors in the images were assessed by two expert doctors. 346 patients' multimodal pictures and labels served as training sets, whereas the remaining 75 patients' multimodal images and labels served as independent test sets. Convolutional neural network (CNN) for modal multidimensional information fusion and multimodal multidimensional information fusion (MMMDF) was used. The three models' performance is compared, and the findings reveal that the multimodal multidimensional fusion model performs best, while the two-modal multidimensional information fusion model performs second. The single-modal multidimensional information fusion model has the poorest performance. In MR images of nasopharyngeal cancer, a convolutional network can precisely and efficiently segment tumors.
对 421 例鼻咽癌患者的头颈部加权磁共振图像进行了采集，由两位专家医生对图像中的肿瘤进行了评估。346 例患者的多模态图像及其标签被用作训练集，而其余 75 例患者的多模态图像及其标签被用作独立测试集。使用了用于模态多维信息融合的卷积神经网络（CNN）和多模态多维信息融合（MMMDF）。比较了这三个模型的性能，结果表明，多模态多维融合模型的性能最好，而双模态多维信息融合模型的性能次之。单模态多维信息融合模型的性能最差。在鼻咽癌的磁共振图像中，卷积网络可以精确、高效地分割肿瘤。
