A hybrid FL-enabled ensemble approach for lung disease diagnosis leveraging fusion of SWIN transformer and CNN

Abstract

The significant advancements in computational power create the vast opportunity for using Artificial Intelligence in different applications of healthcare and medical science. A hybrid FL-enabled ensemble approach for lung disease diagnosis leveraging a combination of SWIN transformer and CNN is the combination of cutting-edge technology of AI and Federated Learning. Since, medical specialists and hospitals will have shared data space, based on that data, with the help of Artificial Intelligence and integration of federated learning, we can introduce a secure and distributed system for medical data processing and create an efficient and reliable system. The proposed hybrid model enables the detection of COVID-19 and Pneumonia based on x-ray reports. We will use advanced and the latest available technology that can help to fight against the pandemic that the world has to fight together as a united. We focused on using the latest available CNN models (DenseNet201, Inception V3, VGG 19) and the Transformer model Swin Transformer in order to prepare our hybrid model that can provide a reliable solution as a helping hand for the physician in the medical field. In this thesis, we will discuss how the Federated learning-based Hybrid AI model can improve the accuracy of disease diagnosis and severity prediction of a patient using the real-time continual learning approach and how the integration of federated learning can ensure hybrid model security and keep the authenticity of the information.