V. Devabhaktuni

Uzair Khan, Paheding Sidike, Colin Elkin et al.

Deep learning algorithms have seen acute growth of interest in their applications throughout several fields of interest in the last decade, with medical hyperspectral imaging being a particularly promising domain. So far, to the best of our knowledge, there is no review paper that discusses the implementation of deep learning for medical hyperspectral imaging, which is what this review paper aims to accomplish by examining publications that currently utilize deep learning to perform effective analysis of medical hyperspectral imagery. This paper discusses deep learning concepts that are relevant and applicable to medical hyperspectral imaging analysis, several of which have been implemented since the boom in deep learning. This will comprise of reviewing the use of deep learning for classification, segmentation, and detection in order to investigate the analysis of medical hyperspectral imaging. Lastly, we discuss the current and future challenges pertaining to this discipline and the possible efforts to overcome such trials.

Parashar Dhakal, Praveen Damacharla, Ahmad Y. Javaid et al.

At the time of writing this paper, the world has around eleven million cases of COVID-19, scientifically known as severe acute respiratory syndrome corona-virus 2 (SARS-COV-2). One of the popular critical steps various health organizations are advocating to prevent the spread of this contagious disease is self-assessment of symptoms. Multiple organizations have already pioneered mobile and web-based applications for self-assessment of COVID-19 to reduce this global pandemic's spread. We propose an intelligent voice-based assistant for COVID-19 self-assessment (IVACS). This interactive assistant has been built to diagnose the symptoms related to COVID-19 using the guidelines provided by the Centers for Disease Control and Prevention (CDC) and the World Health Organization (WHO). The empirical testing of the application has been performed with 22 human subjects, all volunteers, using the NASA Task Load Index (TLX), and subjects performance accuracy has been measured. The results indicate that the IVACS is beneficial to users. However, it still needs additional research and development to promote its widespread application.