Work

Artificial Intelligence for Pneumonia Detection: A Federated Deep Learning Approach in Smart Healthcare

Year: 2025

Type: article

Abstract: Artificial Intelligence (AI) plays an important role in driving innovation in smart healthcare by providing accurate, scalable, and privacy-preserving diagnostic options. Pneumonia is still a major global health issue, and early detection is key to improving patient outcomes. This study proposes a federated deep learning (FL) approach for automatic pneumonia detection using chest X-ray images, considering both diagnostic efficacy and data privacy. Two models were developed and tested: a custom-developed convolutional neural network and a VGG16 transfer learning architecture. The framework evaluates diagnostic efficacy in both centralized and federated scenarios, taking into account heterogeneous client distributions and class imbalance. F1-score and accuracy values for the federated models indicate competitive levels, with F1-scores greater than 0.90 for pneumonia, being robust even when the data is not independent and identically distributed. Results confirm that FL could be tested as a privacy-preserving way to manage medical imaging and intelligence across distributed healthcare. This study provides a potential proof of concept of how to incorporate federated AI into smart healthcare and gives direction toward clinically tested and real-world applications.

Source: Future Internet

DOI: 10.3390/fi17120562

Authors: Ana-Mihaela Vasilevschi, Călin-Alexandru Coman, Marilena Ianculescu, Oana Andreia Coman

Institutions: National Institute for Research and Development in Informatics - ICI Bucharest, Universitatea Națională de Știință și Tehnologie Politehnica București, Carol Davila University of Medicine and Pharmacy

Language: English

Topic: COVID-19 diagnosis using AI

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $1,930