COROID: A Crowdsourcing-based Companion Drones to Tackle Current and Future Pandemics

Abstract

Due to the current COVID-19 virus, which has already been declared a pandemic by the World Health Organization (WHO), we are witnessing the greatest pandemic of the decade. Millions of people are being infected, resulting in thousands of deaths every day across the globe. Even the world's best healthcare-providing countries could not handle the pandemic because of the strain of treating thousands of patients at a time. The count of infections and deaths is increasing at an alarming rate because of the spread of the virus. We believe that innovative technologies could help reduce pandemics to a certain extent until we find a definite solution from the medical field to handle and treat such pandemic situations. Technology innovation has the potential to introduce new technologies that could support people and society during these difficult times. Therefore, this paper proposes the idea of using drones as a companion to tackle current and future pandemics. Our COROID drone is based on the principle of crowdsourcing sensors data of the public's smart devices, which can correlate the reading of the infrared cameras equipped on COROID drones. To the best of our knowledge, this concept has yet to be investigated either as a concept or as a product. Therefore, we believe that the COROID drone is innovative and has a huge potential to tackle COVID-19 and future pandemics.

A Systematic Review on Federated Learning in Medical Image Analysis

“…For such that scenario, data encryption-decryption could be a potential solution to exchange information between participants; however the process could be complex, time consuming and not sustainable [3]. So, instead of bringing the data to the location where the model is trained, why not bring the model to the data (institutions and the hospitals) and train directly there in-house, it allows collaborative learning without centralizing the dataset itself, this is called FL.…”

“…they considered all of the articles which have used all forms of medical data to train their FL models. Similarly in [3], [9], and [6] the authors have included the whole healthcare area to survey and review the papers. Some review articles presented specific medical domains, for example, Naeem et al [10] worked particularly on brain tumor diagnosis using MRI images.…”

“…So, in a horizontal partition the client's database holds many different customers but they are collecting all the same type of data on those customers, in other words "same features, different samples". In vertical FL, it has different customers in both but there is an overlap of those customers and they are collecting different features, more specifically "different features, different samples" [3], [9], [84]. However, in this investigation we focused on the medical image research and found most of the articles were based on HFL.…”