IoT Based Health Machine for COVID-19

The new Corona Virus (COVID-19) is a pandemic of unthinkable scope and magnitude that is posing a significant threat to the medical business worldwide in the twenty-first century. To a greater extent, it has fundamentally altered the texture of life. The growing number of people dying as a result of sickness has instilled fear in the minds of many who are hesitant to seek even basic medical help. And, in light of the recent COVID-19 scenario and the growing number of affected people, researchers began to focus on ways to communicate and monitor patient information remotely in order to reduce the risk of getting infected. The Internet of Things (IoT) is one of the booming technologies in the medical and industrial fields. Patients could benefit from the proposed device because it can monitor and diagnose their health status. This study describes a gadget that measures and records heart rate, body temperature, and CT imaging. These records will be measured and sent to the cloud server using an Arduino device with sensors. © 2022 IEEE.

P2P-COVID-GAN: Classification and Segmentation of COVID-19 Lung Infections From CT Images Using GAN

Early and automatic segmentation of lung infections from computed tomography images of COVID-19 patients is crucial for timely quarantine and effective treatment. However, automating the segmentation of lung infection from CT slices is challenging due to a lack of contrast between the normal and infected tissues. A CNN and GAN-based framework are presented to classify and then segment the lung infections automatically from COVID-19 lung CT slices. In this work, the authors propose a novel method named P2P-COVID-SEG to automatically classify COVID-19 and normal CT images and then segment COVID-19 lung infections from CT images using GAN. The proposed model outperformed the existing classification models with an accuracy of 98.10%. The segmentation results outperformed existing methods and achieved infection segmentation with accurate boundaries. The Dice coefficient achieved using GAN segmentation is 81.11%. The segmentation results demonstrate that the proposed model outperforms the existing models and achieves state-of-the-art performance.