Automatic detection of pneumonia analyzing ultrasound digital images

Barrientos, Ronald; Román-Gonzalez, Avid; Barrientos, Franklin; Solis, Leonardo; Correa, Malena; Pajuelo, Mónica; Anticona, Cynthia; Lavarello, Roberto; Castaneda, Benjamín; Oberhelman, Richard A.; Gilman, Robert H.; Zimic, Mirko

doi:10.1109/concapan.2016.7942375

Cited by 20 publications

(19 citation statements)

References 2 publications

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“…Based on the previous discussion, it can be asserted that LUS imaging is a promising option for the screening, diagnosis, and follow-up of pulmonary diseases. Thus, over the past decade, interest in developing computational tools for computer-assisted analysis of LUS imaging has increased [24][25][26][27][28][29][30]. A brief description of the results reported in these works will be provided in the next section.…”

Section: Motivation Of Lus Utilization For Covid-19 Screeningmentioning

confidence: 99%

“…Lung ultrasound (LUS) is a portable, easy to disinfect, low cost and non-invasive medical imaging tool that can be used to identify lung diseases as an alternative to CT and X-ray [19][20][21][22][23]. Computer-assisted analysis of lung ultrasound imagery is a relatively recent approach that has shown great potential for diagnosing pulmonary conditions (e.g., [24][25][26][27][28][29][30][31]). In the context of the current COVID-19 pandemic, by the time of writing this paper, three works have proposed the use of Deep Learning (DL) models for computer-assisted analysis of COVID-19 LUS imaging [32][33][34].…”

Section: Introductionmentioning

confidence: 99%

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Deep-learning based detection of COVID-19 using lung ultrasound imagery

Diaz-Escobar¹,

et al. 2021

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Background The COVID-19 pandemic has exposed the vulnerability of healthcare services worldwide, especially in underdeveloped countries. There is a clear need to develop novel computer-assisted diagnosis tools to provide rapid and cost-effective screening in places where massive traditional testing is not feasible. Lung ultrasound is a portable, easy to disinfect, low cost and non-invasive tool that can be used to identify lung diseases. Computer-assisted analysis of lung ultrasound imagery is a relatively recent approach that has shown great potential for diagnosing pulmonary conditions, being a viable alternative for screening and diagnosing COVID-19. Objective To evaluate and compare the performance of deep-learning techniques for detecting COVID-19 infections from lung ultrasound imagery. Methods We adapted different pre-trained deep learning architectures, including VGG19, InceptionV3, Xception, and ResNet50. We used the publicly available POCUS dataset comprising 3326 lung ultrasound frames of healthy, COVID-19, and pneumonia patients for training and fine-tuning. We conducted two experiments considering three classes (COVID-19, pneumonia, and healthy) and two classes (COVID-19 versus pneumonia and COVID-19 versus non-COVID-19) of predictive models. The obtained results were also compared with the POCOVID-net model. For performance evaluation, we calculated per-class classification metrics (Precision, Recall, and F1-score) and overall metrics (Accuracy, Balanced Accuracy, and Area Under the Receiver Operating Characteristic Curve). Lastly, we performed a statistical analysis of performance results using ANOVA and Friedman tests followed by post-hoc analysis using the Wilcoxon signed-rank test with the Holm’s step-down correction. Results InceptionV3 network achieved the best average accuracy (89.1%), balanced accuracy (89.3%), and area under the receiver operating curve (97.1%) for COVID-19 detection from bacterial pneumonia and healthy lung ultrasound data. The ANOVA and Friedman tests found statistically significant performance differences between models for accuracy, balanced accuracy and area under the receiver operating curve. Post-hoc analysis showed statistically significant differences between the performance obtained with the InceptionV3-based model and POCOVID-net, VGG19-, and ResNet50-based models. No statistically significant differences were found in the performance obtained with InceptionV3- and Xception-based models. Conclusions Deep learning techniques for computer-assisted analysis of lung ultrasound imagery provide a promising avenue for COVID-19 screening and diagnosis. Particularly, we found that the InceptionV3 network provides the most promising predictive results from all AI-based techniques evaluated in this work. InceptionV3- and Xception-based models can be used to further develop a viable computer-assisted screening tool for COVID-19 based on ultrasound imagery.

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Section: Motivation Of Lus Utilization For Covid-19 Screeningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep-learning based detection of COVID-19 using lung ultrasound imagery

Diaz-Escobar¹,

et al. 2021

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“…The network can count the number of B-lines, but it is not able to identify their location. A similar approach is used to analyze brightness profiles from LUS data with artificial neural networks (ANN) in Barrientos et al (2016) , Correa et al (2018) .…”

Section: Segmentation Of Covid-19 Manifestations In Lusmentioning

confidence: 99%

Sonographic Diagnosis of COVID-19: A Review of Image Processing for Lung Ultrasound

McDermott

Łącki

Sainsbury³

et al. 2021

Front. Big Data

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The sustained increase in new cases of COVID-19 across the world and potential for subsequent outbreaks call for new tools to assist health professionals with early diagnosis and patient monitoring. Growing evidence around the world is showing that lung ultrasound examination can detect manifestations of COVID-19 infection. Ultrasound imaging has several characteristics that make it ideally suited for routine use: small hand-held systems can be contained inside a protective sheath, making it easier to disinfect than X-ray or computed tomography equipment; lung ultrasound allows triage of patients in long term care homes, tents or other areas outside of the hospital where other imaging modalities are not available; and it can determine lung involvement during the early phases of the disease and monitor affected patients at bedside on a daily basis. However, some challenges still remain with routine use of lung ultrasound. Namely, current examination practices and image interpretation are quite challenging, especially for unspecialized personnel. This paper reviews how lung ultrasound (LUS) imaging can be used for COVID-19 diagnosis and explores different image processing methods that have the potential to detect manifestations of COVID-19 in LUS images. Then, the paper reviews how general lung ultrasound examinations are performed before addressing how COVID-19 manifests itself in the images. This will provide the basis to study contemporary methods for both segmentation and classification of lung ultrasound images. The paper concludes with a discussion regarding practical considerations of lung ultrasound image processing use and draws parallels between different methods to allow researchers to decide which particular method may be best considering their needs. With the deficit of trained sonographers who are working to diagnose the thousands of people afflicted by COVID-19, a partially or totally automated lung ultrasound detection and diagnosis tool would be a major asset to fight the pandemic at the front lines.

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“…Abdullah et al [120] proposed a detection method for the pneumonia symptoms by using the CNNs based on the difference of gray-scale color and the segmentation between normal and suspicious lung regions. Correa [121,130] introduced a method of automatic diagnosis of pneumonia by pulmonary ultrasound imaging. Different from Refs.…”

Section: Pneumoniamentioning

confidence: 99%

“…Different from Refs. [131] and [130], Cisnerosvelarde et al [122] applied pneumonia detection in a new field based on ultrasound videos rather than ultrasound images. To determine an automated diagnostic method for medical images, 40 simulated chest CXRs related to the normal and pneumonia patients were studied [123].…”

Section: Pneumoniamentioning

confidence: 99%

Survey on deep learning for pulmonary medical imaging

Song

Tian

et al. 2019

Front. Med.

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As a promising method in artificial intelligence, deep learning has been proven successful in several domains ranging from acoustics and images to natural language processing. With medical imaging becoming an important part of disease screening and diagnosis, deep learning-based approaches have emerged as powerful techniques in medical image areas. In this process, feature representations are learned directly and automatically from data, leading to remarkable breakthroughs in the medical field. Deep learning has been widely applied in medical imaging for improved image analysis. This paper reviews the major deep learning techniques in this time of rapid evolution and summarizes some of its key contributions and state-of-the-art outcomes. The topics include classification, detection, and segmentation tasks on medical image analysis with respect to pulmonary medical images, datasets, and benchmarks. A comprehensive overview of these methods implemented on various lung diseases consisting of pulmonary nodule diseases, pulmonary embolism, pneumonia, and interstitial lung disease is also provided. Lastly, the application of deep learning techniques to the medical image and an analysis of their future challenges and potential directions are discussed.

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Automatic detection of pneumonia analyzing ultrasound digital images

Cited by 20 publications

References 2 publications

Deep-learning based detection of COVID-19 using lung ultrasound imagery

Deep-learning based detection of COVID-19 using lung ultrasound imagery

Sonographic Diagnosis of COVID-19: A Review of Image Processing for Lung Ultrasound

Survey on deep learning for pulmonary medical imaging

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