AFM-DViT: A framework for IoT-driven medical image analysis
Year: 2024
Type: article
Abstract: With the rise of Internet of Things (IoT) in healthcare, automated medical image analysis has become essential for real-time disease detection. However, current models face limitations in handling diverse datasets and ensuring privacy across distributed systems. To address these challenges, we propose the AFM-DViT model, which integrates adaptive federated learning with Vision Transformer, significantly enhancing diagnostic accuracy and efficiency in IoT-based settings. Our framework not only improves detection capabilities but also effectively addresses critical issues related to data privacy and heterogeneity in medical imaging. Experimental results demonstrate that AFM-DViT outperforms state-of-the-art methods by achieving an AUROC of 0.841 and sensitivity of 0.925 on the ChestX-ray14 dataset, alongside a sensitivity of 0.888 with an AUC of 0.905 on the LUNA16 dataset. These results highlight the model’s robust detection accuracy while maintaining data privacy. The AFM-DViT model offers an effective solution for secure and efficient medical image analysis in IoT-enabled environments.
Source: Alexandria Engineering Journal
Author: Jiacheng Yang
Institution: Lanzhou University of Finance and Economics
Language: English
Topic: COVID-19 diagnosis using AI
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $1,300
OpenAlex ID: W4404582103