A federated learning framework for pneumonia image detection using distributed data

Abstract

Pneumonia is one of the serious diseases affecting the lungs. Yearly, over four million people die on average. Therefore, it is essential to have an effective system for early diagnoses. State-of-the-art computer-aided Machine Learning (ML) techniques have been used for pneumonia detection. However, pneumonia X-ray images are visually heterogeneous and complex in pattern recognition. Therefore, a vast amount of dataset is required for effective ML model training. The larger data volume can be collected using the real-time dataset from hospitals and medical institutions. However, due to General Data Protection Regulation (GDPR) and the Data Protection Act (DPA), data sharing is not allowed by the third party. This study is inspired by using real-time datasets in a privacy-preserving fashion while using the framework of federated learning (FL). We have performed experiments using state-of-the-art ML models for medical image classification, including pre-trained Convolutional Neural Network (CNN) models of Alexnet, DenseNet, Residual Neural Network-50 (ResNet50), Inception, and Visual Geometry Group-19 (VGG19). The experiments are performed individually on the models and the FL framework. We compared the results using the evaluation metrics and Area Under the Curve (AUC). The preliminary results show the ResNet-50 stands out in performance on the testing dataset producing an accuracy of 93% significantly.

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ORCID for Haiming Liu: orcid.org/0000-0002-0390-3657

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