A Blockchain-Enabled Machine Learning Mask Detection method for Prevention of Pandemic Diseases
Anwar Ali Sathio, Shafiq Ahmed Awan, Ali Orangzeb Panhwar, Ali Aamir, Ariz Muhammad Brohi, Asadullah Burdi
Abstract
During the COVID-19 pandemic, finding effective methods to prevent the spread of infectious diseases has become critical. One important measure for reducing the transmission of airborne viruses is wearing face masks but enforcing mask-wearing regulations can be difficult in many settings. Real-time and accurate monitoring of mask usage is needed to address this challenge. To do so, we propose a method for mask detection using a convolutional neural network (CNN) and blockchain technology. Our system involves training a CNN model on a dataset of images of people with and without masks and then deploying it on IoT-enabled devices for real-time monitoring. The use of blockchain technology ensures the security and privacy of the data and enables the efficient sharing of resources among network participants. Our proposed system achieved 99% accuracy through CNN training and was transformed into a blockchain-enabled network mechanism with QR validation of every node for authentication. This approach has the potential to be an effective tool for promoting compliance with mask-wearing regulations and reducing the risk of infection. We present a framework for implementing this technique and discuss its potential benefits and challenges