Texture Analysis of Ultrasound Images for Pneumonia Detection in Pediatric Patients

Contreras-Ojeda, S. L.; Sierra-Pardo, C.; Domínguez-Jiménez, J.A.; Lopez-Bueno, J.; Contreras-Ortiz, Sonia H.

doi:10.1109/stsiva.2019.8730238

Cited by 12 publications

(4 citation statements)

References 5 publications

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“…Chest X-Rays can be utilized to detect respiratory disorders like Tuberculosis, lung carcinoma and Pneumonia using segmentation, feature extraction and classification using artificial neural network techniques, as described in (Khobragade et al, 2016). Texture features of cost effective ultra sound images are analyzed to detect Pneumonia, as described in (Contreras-Ojeda et al, 2019), where Mean, Median, Standard Deviation, etc. are extracted.…”

Section: Related Workmentioning

confidence: 99%

RETRACTED: Detection of Pneumonia from Chest X-Ray images using Machine Learning

Sureshkumar¹,

Varalakshmi

Yuvaraj

et al. 2022

Concurrent Engineering

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The survival percentage of lung patients can be improved if pneumonia is detected early. Images of the chest X-ray (CXR) are the most common way of identifying and diagnosing pneumonia. A competent radiologist faces a difficult problem in detecting pneumonia from CXR images. Many people are at danger of contracting pneumonia, especially in developing countries where billions of people live in energy poverty and rely on polluting energy sources. Though there are effective tools in existence to prevent, diagnose and treat pneumonia, pneumonia-related deaths are prevalent in most of the countries. But only a small amount of health budgets is allocated to eradicate pneumonia. If the diagnosis of the disease is made in more reliable and cost effective way, tackling the disease won’t be a herculean task. Machine learning algorithms paved a great way to easily identify, diagnose and predict the disease with minimal amount of time. This paper represents the identification of pneumonia from chest X-Ray by implementing traditional machine learning algorithms with ensemble using optimal number of image features with the help of correlation co-efficient. Also deep learning approach has been implemented. The proposed method traditional machine learning approach and deep learning approach achieved accuracy rates of 93.57% and 93.59% and time required for pneumonia detection is 157,452 s (approx.) and 240,253 s (approx.) respectively.

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Section: Related Workmentioning

confidence: 99%

RETRACTED: Detection of Pneumonia from Chest X-Ray images using Machine Learning

Sureshkumar¹,

Varalakshmi

Yuvaraj

et al. 2022

Concurrent Engineering

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“…Previous studies evaluate lung ultrasound using classification based on single frames without analyzing the artifacts in the image. 16,17,23 In our work, the target was to evaluate a quantitative sign of health in single frames. The potential of the algorithm shows that this could be useful to successfully quantify the number of A-lines and discard lung damage in determinate areas.…”

Section: Discussionmentioning

confidence: 99%

“…16 Likewise, a different approach was also reported using statistical analysis of image texture features with up to 95% accuracy. 17 In the case of COVID-19, multiple attempts have been made to detect pneumonia. For example, Born et al (2020) implemented a convolutional neural network (POCOVID-Net) to detect lung damage associated with COVID-19 in US images, 18 obtaining an accuracy of 89% and specificity of 79%, while Khuzani et al (2021) implemented a dimensionality reduction approach with a neural network to automatically detect COVID-19 in chest radiography, obtaining an accuracy of 96% and specificity of 100%.…”

Section: Introductionmentioning

confidence: 99%

Automatic detection of lung ultrasound artifacts using a deep neural networks approach

Vásquez

Romero²,

Zapana³

et al. 2023

18th International Symposium on Medical Information Processing and Analysis

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The COVID-19 pandemic has challenged many of the healthcare systems around the world. Many patients who have been hospitalized due to this disease develop lung damage. In low and middle-income countries, people living in rural and remote areas have very limited access to adequate health care. Ultrasound is a safe, portable and accessible alternative; however, it has limitations such as being operator-dependent and requiring a trained professional. The use of lung ultrasound volume sweep imaging is a potential solution for this lack of physicians. In order to support this protocol, image processing together with machine learning is a potential methodology for an automatic lung damage screening system. In this paper we present an automatic detection of lung ultrasound artifacts using a Deep Neural Network, identifying clinical relevant artifacts such as pleural and A-lines contained in the ultrasound examination taken as part of the clinical screening in patients with suspected lung damage. The model achieved encouraging preliminary results such as sensitivity of 94% , specificity of 81%, and accuracy of 89% to identify the presence of A-lines. Finally, the present study could result in an alternative solution for an operator-independent lung damage screening in rural areas, leading to the integration of AI-based technology as a complementary tool for healthcare professionals.

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“…Для того щоб розв'язати задачу бінарної класифікації (нормапатологія), з кожного фрейму були отримані інформативні ознаки (Feature Engineering). Для цього було застосовано текстурний аналіз, методика якого викладена в працях [3][4][5][6][7]. Кожен фрейм являє собою матрицю пікселів (точок зображення) розміром n  m, де кожне значення пікселя має певний відтінок сірого.…”

Section: результатиunclassified

Random Forest Algorithm Construction for the Diagnosis of Coronary Heart Disease Based on Echocardiography Video Data Streams

Настенко

Maksymenko

Potashev

et al. 2021

Innov Biosyst Bioeng

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Background. Recent studies show that cardiovascular diseases, including coronary heart disease, are the leading causes of death and one of the main factors of disability worldwide. The detection of cases of this type of disease over the past 30 years has increased from 271 million to 523 million and the number of deaths – from 12.1 million to 18.6 million. Cardiovascular diseases are the main cause of death among the population of Ukraine and, according to this indicator, the country remains one of the world leaders. Coronary heart disease is the leading factor in the loss of health in Ukraine and modern diagnostic methods, including machine learning algorithms, are increasingly being used for timely detection. Objective. According to the data of speckle-tracking echocardiography using the random forest method, construct classification algorithms for diagnosing violations of the kinematics of left ventricular contractions in patients with coronary heart disease at rest, and when using an echostress test with a dobutamine test. Methods. Speckle-tracking echocardiography was used to examine 40 patients with coronary heart disease and 16 in whom no cardiac pathology was found. Echocardiography was recorded in B mode in three positions: along the long axis, in 4-chamber, and 2-chamber positions. In total, 6245 frames of the video stream were used: 1871 – without cardiac abnormalities, and 4374 – in the presence of pathology during the examination. 56 patients (2509 frames of video data) were examined without the use of a dobutamine test and 38 patients (3736 frames of video data) – using an echostress test with a dobutamine test if no disturbances were found at rest. Dobutamine doses of 10, 20, and 40 mcg were administered under the supervision of an anesthesiologist. The data of texture analysis of images were used as informative features. To build an algorithm for detecting coronary heart disease the random forest algorithm was applied. Results. At the first stage of the study, the diagnostic algorithms norma–pathology for the state of rest and dobutamine doses of 10, 20, and 40 mcg were constructed. Before applying the algorithm the samples were randomly divided into training (70%) and test (30%). The classifiers were evaluated for accuracy, sensitivity, and specificity. According to the test samples, the accuracy of diagnostic conclusions varied from 97 to 99%. At the second stage of the study, to increase the versatility of the models, the classifier was built for all images, without dividing them into dobutamine doses. The accuracy for the test samples also ranged from 96.6 to 97.8%. To construct diagnostic algorithms by the random forest method the data of texture analysis of images were used. Conclusions. High-precision classification models were obtained using the random forest algorithm. The developed models can be applied to the analysis of echocardiograms obtained in B mode on equipment that is not equipped with the speckle tracking technology.

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Texture Analysis of Ultrasound Images for Pneumonia Detection in Pediatric Patients

Cited by 12 publications

References 5 publications

RETRACTED: Detection of Pneumonia from Chest X-Ray images using Machine Learning

RETRACTED: Detection of Pneumonia from Chest X-Ray images using Machine Learning

Automatic detection of lung ultrasound artifacts using a deep neural networks approach

Random Forest Algorithm Construction for the Diagnosis of Coronary Heart Disease Based on Echocardiography Video Data Streams

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