Deep Analysis of Covid-19 Receptors Recognition and Locating in Pulmonary Ultrasound

Abstract

In the course of the latest COVID-19 flu epidemic, several projects have been carried out to test LD-based strategies for the helping diagnosis of lung diseases. Deeper learning (DL) has proven its effectiveness in radiography. Although the present study relies on CT scans, DL strategies for interpreting pulmonary ultrasound (LUS) images are being used in this article. In specific, we present a new, completely annotated LUS data collection obtained from multiple Italian institutions with labels showing the level of disease intensity in a shot, photo, and digit optimization mask. By using these data, we implement numerous profound models that deal with the related tasks of automated LUS image analysis. We introduce a new deeper network derived from Space Converter Networks, that continuously estimates the extreme disease score for an input frame and weakly controlled the location of pathological machines. We implement also a new approach for efficient video-level averaging of frames based on uninorms. Finally, we benchmark deep state-of-the-art models for estimating COVID-19 biomarker pixel classification. Experiment was conducted on the planned dataset show satisfactory results for all the tasks considered which will pave the way for potential DL studies for the diagnosis of LUS-based COVID-19.