Automatic spine and pelvis detection in frontal X-rays using deep neural networks for patch displacement learning

AbstractA critical step in effective care and treatment planning for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause for the coronavirus disease 2019 (COVID-19) pandemic, is the assessment of the severity of disease progression. Chest x-rays (CXRs) are often used to assess SARS-CoV-2 severity, with two important assessment metrics being extent of lung involvement and degree of opacity. In this proof-of-concept study, we assess the feasibility of computer-aided scoring of CXRs of SARS-CoV-2 lung disease severity using a deep learning system. Data consisted of 396 CXRs from SARS-CoV-2 positive patient cases. Geographic extent and opacity extent were scored by two board-certified expert chest radiologists (with 20+ years of experience) and a 2nd-year radiology resident. The deep neural networks used in this study, which we name COVID-Net S, are based on a COVID-Net network architecture. 100 versions of the network were independently learned (50 to perform geographic extent scoring and 50 to perform opacity extent scoring) using random subsets of CXRs from the study, and we evaluated the networks using stratified Monte Carlo cross-validation experiments. The COVID-Net S deep neural networks yielded R$$^2$$ 2 of $$0.664 \pm 0.032$$ 0.664 ± 0.032 and $$0.635 \pm 0.044$$ 0.635 ± 0.044 between predicted scores and radiologist scores for geographic extent and opacity extent, respectively, in stratified Monte Carlo cross-validation experiments. The best performing COVID-Net S networks achieved R$$^2$$ 2 of 0.739 and 0.741 between predicted scores and radiologist scores for geographic extent and opacity extent, respectively. The results are promising and suggest that the use of deep neural networks on CXRs could be an effective tool for computer-aided assessment of SARS-CoV-2 lung disease severity, although additional studies are needed before adoption for routine clinical use.

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Generalization of Deep Neural Networks for Chest Pathology Classification in X-Rays Using Generative Adversarial Networks

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) ◽

10.1109/icassp.2018.8461430 ◽

2018 ◽

Cited By ~ 36

Author(s):

Hojjat Salehinejad ◽

Shahrokh Valaee ◽

Tim Dowdell ◽

Errol Colak ◽

Joseph Barfett

Keyword(s):

Neural Networks ◽

Deep Neural Networks ◽

Generative Adversarial Networks ◽

X Rays ◽

Adversarial Networks ◽

Pathology Classification

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Automated Localization of Lung Nodules from Chest X-rays With Deep Neural Networks

International Journal of Radiation Oncology*Biology*Physics ◽

10.1016/j.ijrobp.2020.07.703 ◽

2020 ◽

Vol 108 (3) ◽

pp. e294

Author(s):

B. Maksudov ◽

S. Kiselev ◽

M. Kholiavchenko ◽

T. Mustafaev ◽

R. Kuleev ◽

...

Keyword(s):

Neural Networks ◽

Deep Neural Networks ◽

Lung Nodules ◽

X Rays

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Comparing Deep Neural Networks and Gradient Boosting for Pneumonia Detection Using Chest X-Rays

10.4018/978-1-7998-8455-2.ch003 ◽

2022 ◽

pp. 58-79

Author(s):

Son Nguyen ◽

Matthew Quinn ◽

Alan Olinsky ◽

John Quinn

Keyword(s):

Neural Networks ◽

Prediction Accuracy ◽

Deep Neural Networks ◽

Classical Model ◽

Gradient Boosting ◽

X Rays ◽

Computational Power ◽

Computing Services ◽

Cloud Computing Services ◽

Machine Learning Models

In recent years, with the development of computational power and the explosion of data available for analysis, deep neural networks, particularly convolutional neural networks, have emerged as one of the default models for image classification, outperforming most of the classical machine learning models in this task. On the other hand, gradient boosting, a classical model, has been widely used for tabular structure data and leading data competitions, such as those from Kaggle. In this study, the authors compare the performance of deep neural networks with gradient boosting models for detecting pneumonia using chest x-rays. The authors implement several popular architectures of deep neural networks, such as Resnet50, InceptionV3, Xception, and MobileNetV3, and variants of a gradient boosting model. The authors then evaluate these two classes of models in terms of prediction accuracy. The computation in this study is done using cloud computing services offered by Google Colab Pro.

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DeepCXray: Automatically Diagnosing Diseases on Chest X-Rays Using Deep Neural Networks

IEEE Access ◽

10.1109/access.2018.2875406 ◽

2018 ◽

Vol 6 ◽

pp. 66972-66983 ◽

Cited By ~ 2

Author(s):

Xiuyuan Xu ◽

Quan Guo ◽

Jixiang Guo ◽

Zhang Yi

Keyword(s):

Neural Networks ◽

Deep Neural Networks ◽

X Rays

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The Performance of Deep Neural Networks in Differentiating Chest X-Rays of COVID-19 Patients From Other Bacterial and Viral Pneumonias

Frontiers in Medicine ◽

10.3389/fmed.2020.00550 ◽

2020 ◽

Vol 7 ◽

Cited By ~ 2

Author(s):

Mohamed Elgendi ◽

Muhammad Umer Nasir ◽

Qunfeng Tang ◽

Richard Ribon Fletcher ◽

Newton Howard ◽

...

Keyword(s):

Neural Networks ◽

Deep Neural Networks ◽

X Rays

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Identifying Skeletal Maturity from X-rays using Deep Neural Networks

The Open Biomedical Engineering Journal ◽

10.2174/1874120702115010141 ◽

2021 ◽

Vol 15 (1) ◽

pp. 141-148

Author(s):

Suprava Patnaik ◽

Sourodip Ghosh ◽

Richik Ghosh ◽

Shreya Sahay

Keyword(s):

Neural Network ◽

Neural Networks ◽

Deep Neural Network ◽

Deep Neural Networks ◽

Skeletal Maturity ◽

Bone Age ◽

Network Architectures ◽

X Rays ◽

X Ray ◽

Regression Techniques

Skeletal maturity estimation is routinely evaluated by pediatrics and radiologists to assess growth and hormonal disorders. Methods integrated with regression techniques are incompatible with low-resolution digital samples and generate bias, when the evaluation protocols are implemented for feature assessment on coarse X-Ray hand images. This paper proposes a comparative analysis between two deep neural network architectures, with the base models such as Inception-ResNet-V2 and Xception-pre-trained networks. Based on 12,611 hand X-Ray images of RSNA Bone Age database, Inception-ResNet-V2 and Xception models have achieved R-Squared value of 0.935 and 0.942 respectively. Further, in the same order, the MAE accomplished by the two models are 12.583 and 13.299 respectively, when subjected to very few training instances with negligible chances of overfitting.

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Role of Hybrid Deep Neural Networks (HDNNs), Computed Tomography, and Chest X-rays for the Detection of COVID-19

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18063056 ◽

2021 ◽

Vol 18 (6) ◽

pp. 3056

Author(s):

Muhammad Irfan ◽

Muhammad Aksam Iftikhar ◽

Sana Yasin ◽

Umar Draz ◽

Tariq Ali ◽

...

Keyword(s):

Neural Network ◽

Computed Tomography ◽

Neural Networks ◽

Deep Neural Network ◽

Deep Neural Networks ◽

Confusion Matrix ◽

X Rays ◽

Data Repositories ◽

X Ray ◽

Chest X Ray

COVID-19 syndrome has extensively escalated worldwide with the induction of the year 2020 and has resulted in the illness of millions of people. COVID-19 patients bear an elevated risk once the symptoms deteriorate. Hence, early recognition of diseased patients can facilitate early intervention and avoid disease succession. This article intends to develop a hybrid deep neural networks (HDNNs), using computed tomography (CT) and X-ray imaging, to predict the risk of the onset of disease in patients suffering from COVID-19. To be precise, the subjects were classified into 3 categories namely normal, Pneumonia, and COVID-19. Initially, the CT and chest X-ray images, denoted as ‘hybrid images’ (with resolution 1080 × 1080) were collected from different sources, including GitHub, COVID-19 radiography database, Kaggle, COVID-19 image data collection, and Actual Med COVID-19 Chest X-ray Dataset, which are open source and publicly available data repositories. The 80% hybrid images were used to train the hybrid deep neural network model and the remaining 20% were used for the testing purpose. The capability and prediction accuracy of the HDNNs were calculated using the confusion matrix. The hybrid deep neural network showed a 99% classification accuracy on the test set data.

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