Kareem, A.; Liu, H.; Velisavljevic, V. A Privacy-Preserving Approach to Effectively Utilize Distributed Data for Malaria Image Detection. Bioengineering 2024, 11, 340, doi:10.3390/bioengineering11040340.
Kareem, A.; Liu, H.; Velisavljevic, V. A Privacy-Preserving Approach to Effectively Utilize Distributed Data for Malaria Image Detection. Bioengineering 2024, 11, 340, doi:10.3390/bioengineering11040340.
Kareem, A.; Liu, H.; Velisavljevic, V. A Privacy-Preserving Approach to Effectively Utilize Distributed Data for Malaria Image Detection. Bioengineering 2024, 11, 340, doi:10.3390/bioengineering11040340.
Kareem, A.; Liu, H.; Velisavljevic, V. A Privacy-Preserving Approach to Effectively Utilize Distributed Data for Malaria Image Detection. Bioengineering 2024, 11, 340, doi:10.3390/bioengineering11040340.
Abstract
Malaria is one of the life-threatening disease caused by the Anopheles mosquitoes affecting the human red blood cells. It is the global health concern which is quite common in tropical as well as sub-tropical parts of the World. Therefore, it is an important to have an effective computer aided system in place for early detection and treatment. State of the art machine learning techniques are used to detect the infected malaria cells from the pool of images. 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 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 is inspired to use the real-time medical image data while using the framework of federated learning (FL) framework that will ensure the data privacy while mutual collaboration of hospitals and medical institutes. We have used the state of the art machine learning models that include the Resnet50 and densenet in a federated learning framework. We have experimented both models in different settings 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%.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
