scholarly journals Intelligent Computational Model for the Classification of Covid-19 with Chest Radiography Compared to other Respiratory Diseases

2021 ◽  
Author(s):  
Paula Santos
Keyword(s):  
Computational Model ◽  
Bayesian Information Criterion ◽  
Respiratory Diseases ◽  
Recognition Accuracy ◽  
Information Criterion ◽  
Streptococcus Pneumonia ◽  
X Ray ◽  
Pulmonary Pathology ◽  
Statistical Learning Method

Lung X-ray images, if processed using statistical and computational methods, can distinguish pneumonia from COVID-19. The present work shows that it is possible to extract lung X-ray characteristics to improve the methods of examining and diagnosing patients with suspected COVID-19, distinguishing them from malaria, dengue, H1N1, tuberculosis, and Streptococcus pneumonia. More precisely, an intelligent computational model was developed to process lung X-ray images and classify whether the image is of a patient with COVID-19. The images were processed and extracted their characteristics. These characteristics were the input data for an unsupervised statistical learning method, PCA, and clustering, which identified specific attributes of X-ray images with Covid-19. The introduction of statistical models allowed a fast algorithm, which used the X-means clustering method associated with the Bayesian Information Criterion (CIB). The developed algorithm efficiently distinguished each pulmonary pathology from X-ray images. The method exhibited excellent sensitivity. The average recognition accuracy of COVID-19 was 0.93 ± 0.051.

scholarly journals Marie: Validation of the Artificial Intelligence Model for Covid-19 Detection

2021 ◽  
Vol 13 (4) ◽  
Author(s):  
Valdirene Bento ◽  
Bruno Frederico Salaroli ◽  
Paula Santos
Keyword(s):  
Artificial Intelligence ◽  
Computational Model ◽  
Computational Methods ◽  
Health Professionals ◽  
Recognition Accuracy ◽  
Signs And Symptoms ◽  
Minas Gerais ◽  
Streptococcus Pneumonia ◽  
X Ray

Lung X-ray images, if processed using statistical and computational methods, can distinguish pneumonia from COVID-19. The present work shows that it is possible to extract lung X-ray characteristics to improve the methods of examining and diagnosing patients with suspected COVID-19, distinguishing them from malaria, tuberculosis, and Streptococcus pneumonia. More precisely, an intelligent computational model was developed to process lung X-ray images and classify whether the image is of a patient with COVID-19. In partnership with the municipality of Itapeva, Minas Gerais, we carried out patient analysis and, at the same time, we evolved the algorithms to meet the needs of health professionals and also expand support in tracking COVID-19 in the municipality. In this project we will describe cases and even signs and symptoms that were similar to the clinical performed by the doctor. The average recognition accuracy of COVID-19 was 0.97,4 ± 0.043.

scholarly journals Cosmological test using the Hubble diagram of high-z quasars

2019 ◽  
Vol 489 (1) ◽  
pp. 517-523 ◽  
Author(s):  
Fulvio Melia
Keyword(s):  
Bayesian Information Criterion ◽  
Cold Dark Matter ◽  
Information Criterion ◽  
Hubble Diagram ◽  
High Quality ◽  
Diverse Range ◽  
Comparative Tests ◽  
X Ray ◽  
The Standard Model ◽  
Free Parameters

ABSTRACT It has been known for over three decades that the monochromatic X-ray and UV luminosities in quasars are correlated, though non-linearly. This offers the possibility of using high-z quasars as standard candles for cosmological testing. In this paper, we use a recently assembled, high-quality catalogue of 1598 quasars extending all the way to redshift ∼6, to compare the predictions of the Rh = ct and Lambda cold dark matter (ΛCDM) cosmologies. In so doing, we affirm that the parameters characterizing the correlation depend only weakly on the chosen cosmology, and that both models account very well for the data. Unlike ΛCDM, however, the Rh = ct model has no free parameters for this work, so the Bayesian Information Criterion favours it over ΛCDM with a relative likelihood of $\sim 88{{\ \rm per\ cent}}$ versus $\sim 10{{\ \rm per\ cent}}$. This result is consistent with the outcome of other comparative tests, many of which have shown that Rh = ct is favoured over the standard model based on a diverse range of observations.

COMPLEXITY-BASED CLASSIFICATION OF THE CORONAVIRUS DISEASE (COVID-19)

Fractals ◽  
2020 ◽  
Vol 28 (05) ◽  
pp. 2050114 ◽  
Author(s):  
HAMIDREZA NAMAZI ◽  
VLADIMIR V. KULISH
Keyword(s):  
Respiratory Diseases ◽  
Fractal Theory ◽  
Respiratory Illness ◽  
Shannon’S Entropy ◽  
Complex Structures ◽  
Acute Respiratory Illness ◽  
X Ray ◽  
Shannon's Entropy ◽  
Human Lungs

COVID-19 is a pandemic disease, which massively affected human lives in more than 200 countries. Caused by the coronavirus SARS-CoV-2, this acute respiratory illness affects the human lungs and can easily spread from person to person. Since the disease heavily affects human lungs, analyzing the X-ray images of the lungs may prove to be a powerful tool for disease investigation. In this research, we use the information contained within the complex structures of X-ray images between the cases of COVID-19 and other respiratory diseases, whereas the case of healthy lungs is taken as the reference point. To analyze X-ray images, we benefit from the concept of Shannon’s entropy and fractal theory. Shannon’s entropy is directly related to the amount of information contained within the X-ray images in question, whereas fractal theory is used to analyze the complexity of these images. The results, obtained in this study, show that the method of fractal analysis can detect the level of infection among different respiratory diseases and that COVID-19 has the worst effect on the human lungs. In other words, the complexity of X-ray images is proportional to the severity of the respiratory disease. The method of analysis, employed in this study, can be used even further to analyze how COVID-19 progresses in affected patients.

scholarly journals Rapid classification of commercial teas according to their origin and type using elemental content with X-ray fluorescence (XRF) spectroscopy

2021 ◽  
Vol 4 ◽  
pp. 45-52
Author(s):  
Cia Min Lim ◽  
Manus Carey ◽  
Paul N. Williams ◽  
Anastasios Koidis
Keyword(s):  
Elemental Content ◽  
X Ray ◽  
Xrf Spectroscopy

Hierarchical Scheme for Vehicle Make and Model Recognition

2021 ◽  
pp. 036119812110197
Author(s):  
Chaoqing Wang ◽  
Junlong Cheng ◽  
Yuefei Wang ◽  
Yurong Qian
Keyword(s):  
Intelligent Transportation Systems ◽  
Recognition Accuracy ◽  
Transportation Systems ◽  
Feature Descriptor ◽  
Vehicle Recognition ◽  
Public Dataset ◽  
Classification Of Images ◽  
Model Recognition ◽  
Hierarchical Scheme

A vehicle make and model recognition (VMMR) system is a common requirement in the field of intelligent transportation systems (ITS). However, it is a challenging task because of the subtle differences between vehicle categories. In this paper, we propose a hierarchical scheme for VMMR. Specifically, the scheme consists of (1) a feature extraction framework called weighted mask hierarchical bilinear pooling (WMHBP) based on hierarchical bilinear pooling (HBP) which weakens the influence of invalid background regions by generating a weighted mask while extracting features from discriminative regions to form a more robust feature descriptor; (2) a hierarchical loss function that can learn the appearance differences between vehicle brands, and enhance vehicle recognition accuracy; (3) collection of vehicle images from the Internet and classification of images with hierarchical labels to augment data for solving the problem of insufficient data and low picture resolution and improving the model’s generalization ability and robustness. We evaluate the proposed framework for accuracy and real-time performance and the experiment results indicate a recognition accuracy of 95.1% and an FPS (frames per second) of 107 for the framework for the Stanford Cars public dataset, which demonstrates the superiority of the method and its availability for ITS.

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