Utilizing Federated Learning for Accurate Prediction of Covid-19 from CT Scan Images

Abstract

Recently, the COVID-19 outbreak has spread across the world, leading the World Health Organization to classify it as a global pandemic. To save the patient's life, the COVID-19 symptoms have to be identified. But using an AI (Artificial Intelligence) model to identify COVID-19 symptoms within the allotted time was challenging. The RT-PCR test was found to be inadequate in determining the COVID status of a patient. To determine if the patient has COVID-19 or not, a Computed Tomography Scan (CT scan) of the patient is a better alternative. The challenging task such as dealing with the overfitting problem, handling large training data and accurate prediction of model. Apart from this, it will be challenging to compile and store all the data from various hospitals on the server, though. Federated learning therefore aids in resolving this problem. Certain deep learning models helps to classify the Covid-19. The main objective of this research study is to accurately predict the covid-19 while using federated learning approach with Homomorphic encryption. This research study will have a detailed analysis of certain deep learning models like VGG19, ResNet50, MobileNEtv2, Deep Learning Aggregation (DLA) along with maintaining privacy with encryption.

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