A COVID-19 Prediction Using a Blockchain Approach Integrated With UV-NET for Urban Management

Abstract

Growing cities contributed to the increase in the COVID-19 virus. Consequently, many researchers are exploring the dynamics of the pandemic and to analyse the impacts of pandemic on such cities. The main aim of this research is to understand impacts of the pandemic in Vellore city, Tamil Nadu, India by building a viable solution which can be beneficial for the medical fraternity. A blockchain based approach integrating Artificial Intelligence (AI) is proposed for secure access and storage of Electronic Health Records (EHR) of COVID-19 patients in Vellore city. The blockchain follows the principle of absolute privacy and anonymity of medical records. The decentralized architecture is built to secure from different attacks as the hash of the records are stored in the blockchain. The proposed approach consists of a U-net and V-net model, one for segmenting lungs from the x-rays and second one for segmenting COVID-19 infection patches. The UV-net model is a Convolutional Neural Network (CNN) for fast and precise image segmentation. Experimental analysis is provided on nearly 33,920 chest X-ray images and text records gathered from a hospital in the Vellore city. The proposed model resulted in a precision, recall and F-score of 0.91, 0.87 and 0.89 respectively. The predicted results are manually analysed by the doctor in their login to finally cross verify and conclude the results which are stored in blockchain. This will aid doctors to centrally diagnose the patients and assist in proper treatment for faster recovery.