LAUSR.org creates dashboard-style pages of related content for over 1.5 million academic articles. Sign Up to like articles & get recommendations!

Metadata and Image Features Co-Aware Personalized Federated Learning for Smart Healthcare.

Photo by hajjidirir from unsplash

Recently, artificial intelligence has been widely used in intelligent disease diagnosis and has achieved great success. However, most of the works mainly rely on the extraction of image features but… Click to show full abstract

Recently, artificial intelligence has been widely used in intelligent disease diagnosis and has achieved great success. However, most of the works mainly rely on the extraction of image features but ignore the use of clinical text information of patients, which may limit the diagnosis accuracy fundamentally. In this paper, we propose a metadata and image features co-aware personalized federated learning scheme for smart healthcare. Specifically, we construct an intelligent diagnosis model, by which users can obtain fast and accurate diagnosis services. Meanwhile, a personalized federated learning scheme is designed to utilize the knowledge learned from other edge nodes with larger contributions and customize high-quality personalized classification models for each edge node. Subsequently, a Naïve Bayes classifier is devised for classifying patient metadata. And then the image and metadata diagnosis results are jointly aggregated by different weights to improve the accuracy of intelligent diagnosis. Finally, the simulation results illustrate that, compared with the existing methods, our proposed algorithm achieves better classification accuracy, reaching about 97.16% on PAD-UFES-20 dataset.

Keywords: diagnosis; federated learning; personalized federated; image features; metadata image; image

Journal Title: IEEE journal of biomedical and health informatics
Year Published: 2023

Link to full text (if available)


Share on Social Media:                               Sign Up to like & get
recommendations!

Related content

More Information              News              Social Media              Video              Recommended



                Click one of the above tabs to view related content.