Detection of Lung Ultrasound Covid-19 Disease Patients based Convolution Multifacet Analytics using Deep Learning

Deep Learning techniques for ultrasound images, from the front end to the most advanced applications, are the potential effect of deep learning methods on many aspects of the analysis of the ultrasound images. The Covid-19 epidemic has exposed global health care vulnerabilities, especially in developing countries. Lung Ultra-Sound (LUS) imaging as a real-time analytic tool for lung injuries is superior to X-rays and similar to CT, enabling real-time diagnosis. Relying on operator training and experience is the main limitation of the range. COVID-19 lung ultrasonography mainly reflects the pattern of pneumonia, and pleural effusion is not common. The previous system does not provide image accuracy, clarity, it is cost-effective screening large-scale traditional tests are not possible. To overcome the issues, this work proposed the method Convolutional Multi -Facet Analytics (CMFA) algorithm for using the Lung Ultra-Sound (LUS) imaging. Initially start the Preprocessing step based on the Geometric Image Noise Filtering (GINT) for removed the image noises, and unwanted values from the images, second steps of the image processing for Feature selection using the K-Nearest Neighbor (KNN) and Adaptive Gradient Boosting Algorithm (AGBA) for optimizing the image feature od efficient to reduce the same information form he original dataset. And then bagging with K-Nearest Neighbor (KNN) and Adaptive Gradient Boosting Regression (AGBR) Algorithm estimate the images feature weights like (shape, size, etc.) to test, and verify the best combined classifier model splitting training and testing for feature selection and evaluating the results in Softmax activation function. Classified the train and test features using the Convolutional Multi-Facet Analytics (CMFA) algorithm for analyzing the variety of different important features from the dataset. The simulation results show that Sensitivity, specificity, accuracy, and Error rate score shows better results.