COVID-19 classification of X-ray images using deep neural networks

AbstractBackgroundIn the midst of the coronavirus disease 2019 (COVID-19) outbreak, chest X-ray (CXR) imaging is playing an important role in the diagnosis and monitoring of patients with COVID-19. Machine learning solutions have been shown to be useful for X-ray analysis and classification in a range of medical contexts.PurposeThe purpose of this study is to create and evaluate a machine learning model for diagnosis of COVID-19, and to provide a tool for searching for similar patients according to their X-ray scans.Materials and MethodsIn this retrospective study, a classifier was built using a pre-trained deep learning model (ReNet50) and enhanced by data augmentation and lung segmentation to detect COVID-19 in frontal CXR images collected between January 2018 and July 2020 in four hospitals in Israel. A nearest-neighbors algorithm was implemented based on the network results that identifies the images most similar to a given image. The model was evaluated using accuracy, sensitivity, area under the curve (AUC) of receiver operating characteristic (ROC) curve and of the precision-recall (P-R) curve.ResultsThe dataset sourced for this study includes 2362 CXRs, balanced for positive and negative COVID-19, from 1384 patients (63 +/- 18 years, 552 men). Our model achieved 89.7% (314/350) accuracy and 87.1% (156/179) sensitivity in classification of COVID-19 on a test dataset comprising 15% (350 of 2326) of the original data, with AUC of ROC 0.95 and AUC of the P-R curve 0.94. For each image we retrieve images with the most similar DNN-based image embeddings; these can be used to compare with previous cases.ConclusionDeep Neural Networks can be used to reliably classify CXR images as COVID-19 positive or negative. Moreover, the image embeddings learned by the network can be used to retrieve images with similar lung findings.SummaryDeep Neural Networks and can be used to reliably predict chest X-ray images as positive for coronavirus disease 2019 (COVID-19) or as negative for COVID-19.Key ResultsA machine learning model was able to detect chest X-ray (CXR) images of patients tested positive for coronavirus disease 2019 with accuracy of 89.7%, sensitivity of 87.1% and area under receiver operating characteristic curve of 0.95.A tool was created for finding existing CXR images with imaging characteristics most similar to a given CXR, according to the model’s image embeddings.

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Deep Neural Networks for Automatic Classification of Knee Osteoarthritis Severity Based on X-ray Images

2020 The 8th International Conference on Information Technology: IoT and Smart City ◽

10.1145/3446999.3447020 ◽

2020 ◽

Author(s):

Rima Tri Wahyuningrum ◽

Achmad Yasid ◽

Gijbertus Jacob Verkerke

Keyword(s):

Neural Networks ◽

Knee Osteoarthritis ◽

Deep Neural Networks ◽

Automatic Classification ◽

X Ray

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Automatic Evaluation of the Lung Condition of COVID-19 Patients Using X-ray Images and Convolutional Neural Networks

Journal of Personalized Medicine ◽

10.3390/jpm11010028 ◽

2021 ◽

Vol 11 (1) ◽

pp. 28

Author(s):

Ivan Lorencin ◽

Sandi Baressi Šegota ◽

Nikola Anđelić ◽

Anđela Blagojević ◽

Tijana Šušteršić ◽

...

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Clinical Picture ◽

Data Augmentation ◽

Lower Amount ◽

Training Procedure ◽

X Ray ◽

Severe Clinical Picture ◽

Lung Condition

COVID-19 represents one of the greatest challenges in modern history. Its impact is most noticeable in the health care system, mostly due to the accelerated and increased influx of patients with a more severe clinical picture. These facts are increasing the pressure on health systems. For this reason, the aim is to automate the process of diagnosis and treatment. The research presented in this article conducted an examination of the possibility of classifying the clinical picture of a patient using X-ray images and convolutional neural networks. The research was conducted on the dataset of 185 images that consists of four classes. Due to a lower amount of images, a data augmentation procedure was performed. In order to define the CNN architecture with highest classification performances, multiple CNNs were designed. Results show that the best classification performances can be achieved if ResNet152 is used. This CNN has achieved AUCmacro¯ and AUCmicro¯ up to 0.94, suggesting the possibility of applying CNN to the classification of the clinical picture of COVID-19 patients using an X-ray image of the lungs. When higher layers are frozen during the training procedure, higher AUCmacro¯ and AUCmicro¯ values are achieved. If ResNet152 is utilized, AUCmacro¯ and AUCmicro¯ values up to 0.96 are achieved if all layers except the last 12 are frozen during the training procedure.

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Inclusion of Multiple Cycling of the Potential into Deep Neural Network Classification of Voltammetric Reaction Mechanisms

Faraday Discussions ◽

10.1039/d1fd00050k ◽

2021 ◽

Author(s):

Luke Gundry ◽

Gareth Kennedy ◽

Alan Bond ◽

Jie Zhang

Keyword(s):

Neural Network ◽

Neural Networks ◽

Reaction Mechanisms ◽

Deep Neural Network ◽

Deep Neural Networks ◽

Neural Network Classification ◽

Initial Cycle

The use of Deep Neural Networks (DNNs) for the classification of electrochemical mechanisms based on training with simulations of the initial cycle of potential have been reported. In this paper,...

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