Privacy-Preserving Multi-Label Chest Disease Classification using CLAHE-Enhanced ResNeSt-50 in a Federated Learning Framework

Abstract

Deep learning based medical diagnosis systems are often hindered by privacy restrictions on patients’ health data. In this paper, we propose a privacy preserving decentralized algorithm for multi-label classification of chest diseases using Federated Learning (FL). We experimented on 26,218 images sampled from NIH Chest X-ray dataset with a ResNeSt-50 deep neural network to classify 8 thoracic abnormalities. ResNeSt-50 incorporates Split-Attention mechanisms, which improve multiscale feature extraction for chest disease classification. As chest x-rays are low contrast images by nature, we applied Contrast Limited Adaptive Histogram Equalization (CLAHE) preprocessing to input images. The experimental results show that our proposed FL method obtains a competitive accuracy score of 7.04/8.0 (88.01%) compared to the non-decentralized baseline with the added benefits of FL, such as reduction in validation loss. This work aims to serve as a baseline for future work in FL for chest imaging.