Recent publications in the field of medical image fusion point out the value of multi-modality in diagnosis, pre-surgical planning and surgical intervention. The integration of multiple data sources including medical images from different devices or sensors strongly increases reliability and information content. Successfully fused multi-modal data should not contain any artefacts, not remove any relevant information from the original data and minimize redundancy. Image and data fusion aims at providing supplementary clinical information that is not apparent in the individual images alone. Image and data fusion finds many different applications in the fields of remote sensing, military, biometrics, machine vision and medical imaging. The scientific community has established three levels of fusion rules, namely pixel, feature and decision level. Depending on the application, processing technique or available data each level has its importance and proven significance in medical data processing. Each level provides a set of rules that can be applied. The selection of the fusion operator has a strong impact on the quality of the result. It becomes apparent that the selection of level and technique must vary according to the information that needs to be extracted for a certain application. Each technique has its advantages and disadvantages which have to be carefully evaluated. Based on the availability of multimodal devices, such as ultrasound (US), magnetic resonance imaging (MRI) and computed tomography (CT), different images and data of the same object are obtained. The multiple images, the variety of fusion levels and rules lead to an uncountable number of possible combinations. This makes it very difficult for the user to select the most beneficial solution without losing valuable time and resources.
Novel COVID-19 Coronavirus disease, namely SARS-CoV-2, is a global pandemic and has spread to more than 200 countries. The sudden rise in the number of cases is causing a tremendous effect on healthcare services worldwide. To assist strategies in containing its spread, machine learning (ML) has been employed to effectively track the daily infected and mortality cases as well as to predict the peak growth among the states or/and country-wise. The evidence of ML in tackling previous epidemics has encouraged researchers to reciprocate with this outbreak. In this paper, recent studies that apply various ML models in predicting and forecasting COVID-19 trends have been reviewed. The development in ML has significantly supported health experts with improved prediction and forecasting. By developing prediction models, the world can prepare and mitigate the spread and impact against COVID-19.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.