Secured COVID-19 CT image classification based on human-centric IoT and vision transformer

Abstract

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.