Telemedicine in the Era of Covid-19: Teleconsultation Architecture Platform

Teleconsultation is a type of medical practice similar to face-to-face consultations, and it allows a health professional to give a consultation remotely through information and communication technologies. In the context of the management of the coronavirus epidemic, the use of teleconsultation practices can facilitate healthcare access and limit the risk of avoidable propagation in medical cabinets. This paper presents the monitoring of international teleconsultation referrals in the era of Covid-19 to facilitate and prevent the suspension of access to care, the most common architecture for teleconsultation, communication technologies and protocols, vital body signals, video transmission, and the conduct of teleconsultation. The aim is to develop a teleconsultation platform to diagnose the patient in real time, transmit data from the remote location to the doctor, and provide a teleconsultation. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

DETECTION OF OMICRON VIRUS BASED ON CONVOLUTIONAL NEURAL NETWORK

COVID-19, an upper respiratory viral infection, is spreading too quickly and is now found almost everywhere on the planet. A chest x-ray and RT-PCR are used to confirm Covid-19. However, resource constraints in developing countries make this difficult. This paper aims to monitor infection in Egyptian's lungs using chest X-rays and convolutional neural networks in the early-stage detection. The omicron chest X-ray dataset is gathered from public records along with hospital and doctor arrangements that are approved by their patients to achieve our goal. The classification mission's backbone is a classic neural network (CNN), which benefited from its speed while deep networks are being trained and reduced the impact of vanishing gradient problems. Images are resized and pre-processed to improve sharpness and contrast before being used to validate the proposed system. Meanwhile, by feeding images into a deep neural network, infectiousness can be predicted. The deep learning measurement's receiver operating characteristics (ROC) region under the curve is 0.9888, with 96.2 percent sensitivity, 98 percent accuracy, and 100 percent precision, according to the findings. It has been demonstrated that the proposed system can be easily modified to increase efficiency by adding additional images (normal and infected). As a quick alternative to the current PCR-based method, the proposed system offers a significant advantage to all nations in terms of screening and diagnosing OMICRON. © 2022 Little Lion Scientific