An automatic method for lung segmentation and reconstruction in chest X-ray 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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Automated diagnosis of COVID-19 with limited posteroanterior chest X-ray images using fine-tuned deep neural networks

Applied Intelligence ◽

10.1007/s10489-020-01900-3 ◽

2020 ◽

Cited By ~ 5

Author(s):

Narinder Singh Punn ◽

Sonali Agarwal

Keyword(s):

Neural Networks ◽

Deep Neural Networks ◽

Automated Diagnosis ◽

X Ray ◽

Chest X Ray

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Automatic Detection of COVID-19 Disease in Chest X-Ray Images using Deep Neural Networks

2020 IEEE 8th R10 Humanitarian Technology Conference (R10-HTC) ◽

10.1109/r10-htc49770.2020.9357034 ◽

2020 ◽

Author(s):

Mohammad Mahmudur Rahman Khan ◽

Shadman Sakib ◽

Md. Abu Bakr Siddique ◽

Madiha Chowdhury ◽

Ziad Hossain ◽

...

Keyword(s):

Neural Networks ◽

Deep Neural Networks ◽

Automatic Detection ◽

X Ray ◽

Chest X Ray

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Covid-19 Chest X-ray Images: Lung Segmentation and Diagnosis using Neural Networks

International Journal on Computational Science & Applications ◽

10.5121/ijcsa.2020.10501 ◽

2020 ◽

Vol 10 (5) ◽

pp. 1-11

Author(s):

Alan Zhang

Keyword(s):

Neural Networks ◽

Learning Community ◽

World Wide ◽

Lung Region ◽

Dice Coefficient ◽

Training Set ◽

Lung Segmentation ◽

X Ray ◽

Chest X Ray ◽

Validation Set

COVID-19 has caused world-wide disturbances and the machine learning community has been finding ways to combat the disease. Applications of neural networks in image processing tasks allow COVID-19 Chest X-ray images to be meaningfully processed. In this study, the V7 Darwin COVID-19 Chest X-ray Dataset is used to train a U-Net based network that performs lung-region segmentation and a convolutional neural network that performs diagnosis on Chest X-ray images. This dataset is larger than most of the datasets used to develop existing COVID-19 related neural networks. The lung segmentation network achieved an accuracy of 0.9697 on the training set and an accuracy of 0.9575, an Intersectionover-union of 0.8666, and a dice coefficient of 0.9273 on the validation set. The diagnosis network achieved an accuracy of 0.9620 on the training set and an accuracy of 0.9666 and AUC of 0.985 on the validation set.

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Vulnerability of deep neural networks for detecting COVID-19 cases from chest X-ray images to universal adversarial attacks

PLoS ONE ◽

10.1371/journal.pone.0243963 ◽

2020 ◽

Vol 15 (12) ◽

pp. e0243963

Author(s):

Hokuto Hirano ◽

Kazuki Koga ◽

Kazuhiro Takemoto

Keyword(s):

Neural Networks ◽

Deep Neural Networks ◽

Input Image ◽

Careful Consideration ◽

Fine Tuning ◽

Specific Class ◽

Success Rates ◽

Target Class ◽

X Ray ◽

Chest X Ray

Owing the epidemic of the novel coronavirus disease 2019 (COVID-19), chest X-ray computed tomography imaging is being used for effectively screening COVID-19 patients. The development of computer-aided systems based on deep neural networks (DNNs) has become an advanced open source to rapidly and accurately detect COVID-19 cases because the need for expert radiologists, who are limited in number, forms a bottleneck for screening. However, thus far, the vulnerability of DNN-based systems has been poorly evaluated, although realistic and high-risk attacks using universal adversarial perturbation (UAP), a single (input image agnostic) perturbation that can induce DNN failure in most classification tasks, are available. Thus, we focus on representative DNN models for detecting COVID-19 cases from chest X-ray images and evaluate their vulnerability to UAPs. We consider non-targeted UAPs, which cause a task failure, resulting in an input being assigned an incorrect label, and targeted UAPs, which cause the DNN to classify an input into a specific class. The results demonstrate that the models are vulnerable to non-targeted and targeted UAPs, even in the case of small UAPs. In particular, the 2% norm of the UAPs to the average norm of an image in the image dataset achieves >85% and >90% success rates for the non-targeted and targeted attacks, respectively. Owing to the non-targeted UAPs, the DNN models judge most chest X-ray images as COVID-19 cases. The targeted UAPs allow the DNN models to classify most chest X-ray images into a specified target class. The results indicate that careful consideration is required in practical applications of DNNs to COVID-19 diagnosis; in particular, they emphasize the need for strategies to address security concerns. As an example, we show that iterative fine-tuning of DNN models using UAPs improves the robustness of DNN models against UAPs.

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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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