Dynamic Multimodal Fusion for Real-Time Lung Cancer Diagnostics in IoMT-Enabled

The Internet of Medical Things (IoMT) has transformed healthcare by integrating medical devices, wearables, and electronic medical records (EMRs). These technologies enable real-time diagnostics, personalized monitoring, and proactive healthcare delivery. However, applying the IoMT to critical areas like lung cancer detection remains challenging due to multimodal data heterogeneity, real-time processing requirements, and resource constraints nature of the devices. To address these challenges, in this paper, we present a novel IoMT-based framework for lung cancer diagnostics that unifies imaging data, sensor readings, and EMRs into a cohesive pipeline. The proposed framework employs convolutional neural networks, vision transformers for imaging analysis, temporal convolutional networks for time-series sensor data, and an attention-based fusion mechanism for dynamic multimodal integration. These techniques are supported by preprocessing methods such as U-Net++ segmentation and temporal feature extraction to enhance data consistency and efficiency. Additionally, lightweight models are deployed on IoMT devices to ensure scalability and real-time inference, making the framework practical for resource-constrained environments. Extensive evaluation demonstrates that the framework achieves 98% accuracy, 98% F1-score, and 0.99 AUC (area under the curve) of the ROC (receiver operating characteristic (AUC-ROC). These results show the proposed framework outperforms state-of-the-art approaches and showcases its potential for large-scale clinical deployment.