A Federated Learning Approach to Pneumonia Detection
Saadat Hasan Khan, Md. Golam Rabiul Alam
Abstract
Pneumonia Detection has been a real problem for the last few centuries. Detecting Pneumonia has been a job for the skilled, such as doctors and medical practitioners. Visiting doctors in this time in many countries is very tough with Covid-19 on the rise and stricter lockdown regulations. Deep Learning has helped build many systems and algorithms over the years to detect pneumonia using X-ray images. Such Deep Learning models are first trained on many X-ray images that would be collected from multiple hospitals and diagnostic centers and then can be deployed centrally for people to use them. However, building such models is impeded by the problem of garnering mass data from hospitals due to data confidentiality between patients and hospitals. For that, we propose a system where detecting Pneumonia would be done using a Deep Learning model with a Federated Learning approach and achieve an accuracy of around 90%. This will build a central model by training local models in different hospitals with their own data, maintaining all patient data privacy.