Security and privacy are fundamental to applications of medical internet of things (IoT). This article proposes a new computed tomography (CT) image three-classification prediction network, Re50-ViT (ResNet50 and Vision Transformer), which aims to improve the accuracy of traditional neural networks in screening patients with novel coronavirus infection pneumonia. To enhance network performance, the batch normalization layer is replaced with the group normalization layer for more stable activation normalization. The front-end utilizes ResNet50 for local feature extraction, and global information integration is achieved through the connection of a Class token and position embedding. Dropout layer is added to prevent overfitting and improve generalization. multiple transformer encoder layers are used to capture complex patterns and model label relationships within the CT images. The network integrates human-centric IoT and security measures to protect patient privacy and sensitive medical information. Experimental results compared to existing methods demonstrate the superiority of the Re50-ViT network. The Grad-CAM (gradient-weighted class activation mapping) technique provides intuitive visualization, highlighting the importance of specific regions in the CT images. The network shows effectiveness and reliability in detecting lung lesions, including COVID-19 and other pulmonary abnormalities. The integration of human-centric IoT and security considerations further enhances the clinical value of the network while ensuring the protection of patient data and privacy.

安全和隐私是医疗物联网（IoT）应用的基础。本文提出了一种新的计算机断层扫描（CT）图像三分类预测网络Re50-ViT（ResNet50和Vision Transformer），旨在提高传统神经网络在筛查新型冠状病毒感染肺炎患者中的准确性。为了增强网络性能，将批归一化层替换为组归一化层，以实现更稳定的激活归一化。前端利用ResNet50进行局部特征提取，通过Class token和位置嵌入的连接实现全局信息整合。添加Dropout层是为了防止过拟合，提高泛化能力。多个 Transformer 编码器层用于捕获 CT 图像中的复杂模式和模型标签关系。该网络集成了以人为本的物联网和安全措施，以保护患者隐私和敏感医疗信息。与现有方法相比的实验结果证明了 Re50-ViT 网络的优越性。 Grad-CAM（梯度加权类激活映射）技术提供直观的可视化，突出了 CT 图像中特定区域的重要性。该网络在检测肺部病变（包括 COVID-19 和其他肺部异常）方面显示出有效性和可靠性。以人为中心的物联网和安全考虑的整合进一步增强了网络的临床价值，同时确保患者数据和隐私的保护。