ABSTRACT
Continuous patient care and the use of multiple medical machines are two challenges facing today's healthcare sector in terms of patient's healthcare. During the pandemic situation, many people isolated in their home, such as covid-19 positive patients, elderly people living away from their families, bedridden patients, etc., need regular health checks and controls, but during this pandemic is lacking. Recent advances in the Internet of Medical Things (IoMT) has been able to give good results in collecting health data of patients at home environment. Deep learning (DL) applications can able to run on edge nodes, it locally processes, computes and analyzes data from IOMT devices to make inferences on patient health information. This ensures the privacy and security of the patient's physiological information and also and allows patient health information to remain at the patient's side. Send all this information to healthcare professionals and relatives of patients. This framework will provide safety for isolated patients and a health support systemas a whole. © 2022 IEEE.
ABSTRACT
COVID-19 is quickly gaining popularity across the globe. By April 14, 2020, 128,000 individuals had been killed by COVID-19, and 1.99 million incidents had been recorded in 210 countries and regions, totaling 219.747 cases. The rapid spread of the virus throughout the globe has resulted in a severe shortage of medical test kits in many parts of the world, particularly in Africa. A chest X-ray may prove to be a more successful screening method in certain situations than thermal screening of the whole body, due to the fact that the respiratory system is the most susceptible area in a human’s body to infection. Lung segmentation is the initial stage in identifying diseases using a chest x-ray picture. We describe a method for segmenting the lung region from CXR images that is based on the Euler number thresholding approach, i. When compared to current state-of-the-art methods, the suggested method demonstrates superior accuracy and performance. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.