An automated privacy-preserving self-supervised classification of COVID-19 from lung CT scan images minimizing the requirements of large data annotation

This study presents a novel privacy-preserving self-supervised (SSL) framework for COVID-19 classification from lung CT scans, utilizing federated learning (FL) enhanced with Paillier homomorphic encryption (PHE) to prevent third-party attacks during training. The FL-SSL based framework employs two publicly available lung CT scan datasets which are considered as labeled and an unlabeled dataset. The unlabeled dataset is split into three subsets which are assumed to be collected from three hospitals. Training is done using the Bootstrap Your Own Latent (BYOL) contrastive learning SSL framework with a VGG19 encoder followed by attention CNN blocks (VGG19 + attention CNN). The input datasets are processed by selecting the largest lung portion of each lung CT scan using an automated selection approach and a 64 × 64 input size is utilized to reduce computational complexity. Healthcare privacy issues are addressed by collaborative training across decentralized datasets and secure aggregation with PHE, underscoring the effectiveness of this approach. Three subsets of the dataset are used to train the local BYOL model, which together optimizes the central encoder. The labeled dataset is employed to train the central encoder (updated VGG19 + attention CNN), resulting in an accuracy of 97.19%, a precision of 97.43%, and a recall of 98.18%. The reliability of the framework's performance is demonstrated through statistical analysis and five-fold cross-validation. The efficacy of the proposed framework is further showcased by showing its performance on three distinct modality datasets: skin cancer, breast cancer, and chest X-rays. In conclusion, this study offers a promising solution for accurate diagnosis of chest X-rays, preserving privacy and overcoming the challenges of dataset scarcity and computational complexity.