A privacy-preserving approach to effectively utilize distributed data for malaria image detection

Abstract

Malaria is one of the life-threatening disease caused by the parasite knows as Plasmodium falciparum affecting the human red blood cells. Therefore, it is an important to have an effective computer aided system in place for early detection and treatment. As the visual heterogeneity of the malaria dataset is highly complex and dynamic, therefore higher number of images are needed to train the machine learning (ML) models effectively. However, hospitals as well as medical institutions do not share the medical image data for collaboration due to general data protection regulation (GDPR) and data protection act (DPA). To overcome this collaborative challenge, our research utilised real-time medical image data using framework of federated learning (FL) framework. We have used the state of the art ML models that include the Resnet50 and densenet in a federated learning framework. We have experimented both models in different settings on malaria dataset constituting 27,560 publicly available images and our preliminary results showed that the densenet model performed better in accuracy (75%) in contrast to resnet50 (72%) while considering 8 clients, while the trend is observed common in 4 clients with the similar accuracy of 94% and 6 client showed that the densenet model performed quite well with the accuracy of 92% while resnet50 achieving only 72%. The federated learning framework enhances the accuracy due to it’s decentralised nature, continuous learning, effective communication among clients as well as the efficient local adaptation. The use of federated learning architecture among the distinct clients for ensuring the data privacy and following the GDPR is the contribution of this research work.