Automatic diagnosis of COVID-19 related respiratory diseases from speech.

In this work, an attempt is made to propose an intelligent and automatic system to recognize COVID-19 related illnesses from mere speech samples by using automatic speech processing techniques. We used a standard crowd-sourced dataset which was collected by the University of Cambridge through a web based application and an android/iPhone app. We worked on cough and breath datasets individually, and also with a combination of both the datasets.

Simulating Federated Transfer Learning for Lung Segmentation using Modified UNet Model.

Lung segmentation helps doctors in analyzing and diagnosing lung diseases effectively. Covid -19 pandemic highlighted the need for such artificial intelligence (AI) model to segment Lung X-ray images and diagnose patient covid conditions, in a short time, which was not possible due to huge number of patient influx at hospitals with the limited radiologist to diagnose based on test report in short time. AI models developed to assist doctors to diagnose faster, faces another challenge of data privacy.