ABSTRACT
Currently, the relevance of remote express diagnostics of various diseases is beyond doubt. The active spread of various types of epidemics and pandemics necessitates the improvement of various types of express diagnostics. The authors conduct research in the field of remote visual diagnostics using modern methods of processing telemedicine video information. This paper discusses the possibility of improving the quality of visualization of diagnostic signs using the digital dermatoscopy method for express diagnostics of skin rashes in COVID-19 in comparison with the manifestations of atopic dermatitis. The prospect of this work is the study of illumination conditions during registration and selection of skin areas for the analysis of diagnostic images. © 2021 IEEE.
ABSTRACT
An effective and accurate method of detecting COVID-19 infection is to analyze medical diagnostic images (e.g. CT scans). However, patients' information is privacy, and it is illegal to share diagnostic images among medical institutions. In this case, a critical issue faced by the model that detects the CT images is lacking enough training images dataset, then the features of COVID-19 cannot be accurately obtained. The data privacy attracts extensive attentions recently and is particularly important for the fast-developing medical institution database and. Considering this point, this paper presents a blockchain federated learning model, which overcomes the burden of centralized collection of large amounts of sensitive data. The model uses a trained model to recognize CT scans, and shares data between hospitals with privacy protection mechanism. This model is able to learn from shared resources or data between different hospital repositories to discover patients with new coronary pneumonia by detecting the computed tomography (CT) images. Finally, we conduct extensive experiments to verify the performance of the model. © 2021 IEEE.