Detection of Lung Ultrasound Covid-19 Disease Patients based Convolution Multifacet Analytics using Deep Learning
2nd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2022
; : 185-190, 2022.
Article
in English
| Scopus | ID: covidwho-1806906
ABSTRACT
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. © 2022 IEEE.
and Adaptive Gradient Boosting Regression (AGBR); Convolutional Multi-Facet Analytics (CMFA); COVID-19; Deep Learning; Geometric Image Noise Filtering (GINT); K-Nearest Neighbor (KNN); Lung Ultra-Sound(LUS); Softmax activation; Adaptive boosting; Biological organs; Classification (of information); Computer aided diagnosis; Computerized tomography; Convolutional neural networks; Cost effectiveness; Deep neural networks; Developing countries; Feature extraction; Image denoising; Image segmentation; Motion compensation; Personnel training; Statistical tests; And adaptive gradient boosting regression; Convolutional multi-facet analytic; Geometric image noise filtering; Gradient boosting; Image noise; K-near neighbor; Lung ultra-sound; Nearest-neighbour; Noise filtering; Convolution
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
2nd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2022
Year:
2022
Document Type:
Article
Similar
MEDLINE
...
LILACS
LIS