ABSTRACT
Over the past two years, the spread of COVID-19 has spurred the use of information and communication technologies (ICT) in aid of healthcare. The need to guarantee continuity to care has promoted research and industry activities aimed at developing solutions for the digitalization of the procedures to be performed to provide health services, even in emergency scenarios. Digital collection, transmission, and processing of health data represent the starting point for fulfilling this innovation process but also bring heterogeneous challenges. These motivations led to the elaboration of this work, which analyzes innovative and technological tools for the development of digital health (eHealth) through the collection of multisectoral literature, produced thanks to the cooperation of varied research groups, thus providing a multidisciplinary survey. Since digital health is expected to be one of the leading applications of the sixth-generation (6G) wireless cellular networks, this paper covers the related telecommunications aspects. Furthermore, the exploitation of artificial intelligence paradigms to elaborate massive amounts of biological data is examined. Given the extreme sensitivity of health data, this paper also investigates security and privacy issues. In particular, the main techniques and approaches to guarantee security properties (i.e., anonymity, responsibility, authentication, confidentiality, integrity, non-repudiation, and revocability) are studied. Applications involving innovative electromagnetic systems for healthcare and assisted living services are described to provide an example of an eHealth scenario leveraging ICT. Finally, the telemedicine-related regulations of the European Commission are analyzed, with particular reference to the General Data Protection Regulation (GDPR).
ABSTRACT
In the Covid-19 era, it is important to have an edge detector for X-ray (XR) images affected by uncertainties with low computational load but with high performance. So, here, a new version of a well-known fuzzy edge detector, in which a new image fuzzification procedure has been formulated, is proposed. The performance were qualitatively/ quantitatively compared with those obtained by Canny's edge detector (gold standard for this type of problem). In addition, an evolution of the deep fuzzy-neural model named CovNNet, recently proposed by the authors to discriminate chest XR (CXR) images of patients with Covid-19 pneumonia from images of patients with interstitial pneumonias not related to Covid-19 (No-Covid-19), is presented and referred as to Enhanced-CovNNet (ECovNNet). Here, the generalization ability of it is also improved by introducing a regularization based on dropping out some nodes of the network in a random way. ECovNNet processes input CXR images and the corresponding fuzzy CXR images (processed through the proposed enhanced-fuzzy edge detector) and extracts relevant CXR/fuzzy features, subsequently combined in a single array named CXR and fuzzy features vector. The latter is used as input to an Autoencoder-(AE)-based classifier to perform the binary classification: Covid-19 and No-Covid-19, reporting accuracy rate up to 81%. Finally, the work is completed with some interesting physico-mathematical results. © 2022 IEEE.