scholarly journals Backdoor Attacks to Deep Neural Network-Based System for COVID-19 Detection from Chest X-ray Images

2021 ◽  
Vol 11 (20) ◽  
pp. 9556
Author(s):  
Yuki Matsuo ◽  
Kazuhiro Takemoto
Keyword(s):  
Open Source ◽  
Training Data ◽  
Fine Tuning ◽  
Security Threat ◽  
Target Class ◽  
X Ray ◽  
Practical Applications ◽  
Targeted Attacks ◽  
Chest X Ray ◽  
Open Source Development

Open-source deep neural networks (DNNs) for medical imaging are significant in emergent situations, such as during the pandemic of the 2019 novel coronavirus disease (COVID-19), since they accelerate the development of high-performance DNN-based systems. However, adversarial attacks are not negligible during open-source development. Since DNNs are used as computer-aided systems for COVID-19 screening from radiography images, we investigated the vulnerability of the COVID-Net model, a representative open-source DNN for COVID-19 detection from chest X-ray images to backdoor attacks that modify DNN models and cause their misclassification when a specific trigger input is added. The results showed that backdoors for both non-targeted attacks, for which DNNs classify inputs into incorrect labels, and targeted attacks, for which DNNs classify inputs into a specific target class, could be established in the COVID-Net model using a small trigger and small fraction of training data. Moreover, the backdoors were effective for models fine-tuned from the backdoored COVID-Net models, although the performance of non-targeted attacks was limited. This indicated that backdoored models could be spread via fine-tuning (thereby becoming a significant security threat). The findings showed that emphasis is required on open-source development and practical applications of DNNs for COVID-19 detection.

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

PLoS ONE ◽  
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.

scholarly journals Fine-Tuning Convolutional Neural Networks for COVID-19 Detection from Chest X-ray Images

Diagnostics ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1887
Author(s):  
Wentao Zhao ◽  
Wei Jiang ◽  
Xinguo Qiu
Keyword(s):  
A Priori ◽  
Clinical Information ◽  
Screening Strategy ◽  
Training Data ◽  
Fine Tuning ◽  
X Ray ◽  
Chest X Ray ◽  
Polymerase Chain ◽  
Improved Performance ◽  
Activation Mapping

As the COVID-19 pandemic continues to ravage the world, the use of chest X-ray (CXR) images as a complementary screening strategy to reverse transcription-polymerase chain reaction (RT-PCR) testing continues to grow owing to its routine clinical application to respiratory diseases. We performed extensive convolutional neural network (CNN) fine-tuning experiments and identified that models pretrained on larger out-of-domain datasets show an improved performance. This suggests that a priori knowledge of models from out-of-field training should also apply to X-ray images. With appropriate hyperparameters selection, we found that higher resolution images carry more clinical information, and the use of mixup in training improved the performance of the model. The experimental showed that our proposed transfer learning present state-of-the-art results. Furthermore, we evaluated the performance of our model with a small amount of downstream training data and found that the model still performed well in COVID-19 identification. We also explored the mechanism of model detection using a gradient-weighted class activation mapping (Grad-CAM) method for CXR imaging to interpret the detection of radiology images. The results helped us understand how the model detects COVID-19, which can be used to discover new visual features and assist radiologists in screening.

scholarly journals COVID-19 diagnosis from chest X-ray images using transfer learning: Enhanced performance by debiasing dataloader

2021 ◽  
Vol 29 (1) ◽  
pp. 19-36
Author(s):  
Çağín Polat ◽  
Onur Karaman ◽  
Ceren Karaman ◽  
Güney Korkmaz ◽  
Mehmet Can Balcı ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Critical Time ◽  
Image Data ◽  
Mapping Method ◽  
Fine Tuning ◽  
Learning Stage ◽  
X Ray ◽  
Testing Dataset ◽  
X Ray Imaging ◽  
Chest X Ray

BACKGROUND: Chest X-ray imaging has been proved as a powerful diagnostic method to detect and diagnose COVID-19 cases due to its easy accessibility, lower cost and rapid imaging time. OBJECTIVE: This study aims to improve efficacy of screening COVID-19 infected patients using chest X-ray images with the help of a developed deep convolutional neural network model (CNN) entitled nCoV-NET. METHODS: To train and to evaluate the performance of the developed model, three datasets were collected from resources of “ChestX-ray14”, “COVID-19 image data collection”, and “Chest X-ray collection from Indiana University,” respectively. Overall, 299 COVID-19 pneumonia cases and 1,522 non-COVID 19 cases are involved in this study. To overcome the probable bias due to the unbalanced cases in two classes of the datasets, ResNet, DenseNet, and VGG architectures were re-trained in the fine-tuning stage of the process to distinguish COVID-19 classes using a transfer learning method. Lastly, the optimized final nCoV-NET model was applied to the testing dataset to verify the performance of the proposed model. RESULTS: Although the performance parameters of all re-trained architectures were determined close to each other, the final nCOV-NET model optimized by using DenseNet-161 architecture in the transfer learning stage exhibits the highest performance for classification of COVID-19 cases with the accuracy of 97.1 %. The Activation Mapping method was used to create activation maps that highlights the crucial areas of the radiograph to improve causality and intelligibility. CONCLUSION: This study demonstrated that the proposed CNN model called nCoV-NET can be utilized for reliably detecting COVID-19 cases using chest X-ray images to accelerate the triaging and save critical time for disease control as well as assisting the radiologist to validate their initial diagnosis.

scholarly journals Detection of COVID-19 Using Transfer Learning and Grad-CAM Visualization on Indigenously Collected X-ray Dataset

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5813
Author(s):  
Muhammad Umair ◽  
Muhammad Shahbaz Khan ◽  
Fawad Ahmed ◽  
Fatmah Baothman ◽  
Fehaid Alqahtani ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Input Image ◽  
Fine Tuning ◽  
Feature Maps ◽  
Virus Family ◽  
X Ray ◽  
Normal Chest ◽  
Chest X Ray ◽  
Confusion Matrices

The COVID-19 outbreak began in December 2019 and has dreadfully affected our lives since then. More than three million lives have been engulfed by this newest member of the corona virus family. With the emergence of continuously mutating variants of this virus, it is still indispensable to successfully diagnose the virus at early stages. Although the primary technique for the diagnosis is the PCR test, the non-contact methods utilizing the chest radiographs and CT scans are always preferred. Artificial intelligence, in this regard, plays an essential role in the early and accurate detection of COVID-19 using pulmonary images. In this research, a transfer learning technique with fine tuning was utilized for the detection and classification of COVID-19. Four pre-trained models i.e., VGG16, DenseNet-121, ResNet-50, and MobileNet were used. The aforementioned deep neural networks were trained using the dataset (available on Kaggle) of 7232 (COVID-19 and normal) chest X-ray images. An indigenous dataset of 450 chest X-ray images of Pakistani patients was collected and used for testing and prediction purposes. Various important parameters, e.g., recall, specificity, F1-score, precision, loss graphs, and confusion matrices were calculated to validate the accuracy of the models. The achieved accuracies of VGG16, ResNet-50, DenseNet-121, and MobileNet are 83.27%, 92.48%, 96.49%, and 96.48%, respectively. In order to display feature maps that depict the decomposition process of an input image into various filters, a visualization of the intermediate activations is performed. Finally, the Grad-CAM technique was applied to create class-specific heatmap images in order to highlight the features extracted in the X-ray images. Various optimizers were used for error minimization purposes. DenseNet-121 outperformed the other three models in terms of both accuracy and prediction.

scholarly journals Evaluation of Scalability and Degree of Fine-Tuning of Deep Convolutional Neural Networks for COVID-19 Screening on Chest X-ray Images Using Explainable Deep-Learning Algorithm

2020 ◽  
Vol 10 (4) ◽  
pp. 213 ◽  
Author(s):  
Ki-Sun Lee ◽  
Jae Young Kim ◽  
Eun-tae Jeon ◽  
Won Suk Choi ◽  
Nan Hee Kim ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Strategies ◽  
Transfer Learning ◽  
Learning Algorithm ◽  
Fine Tuning ◽  
Deep Convolutional Neural Networks ◽  
X Ray ◽  
Backbone Network ◽  
Deep Learning Algorithm ◽  
Chest X Ray

According to recent studies, patients with COVID-19 have different feature characteristics on chest X-ray (CXR) than those with other lung diseases. This study aimed at evaluating the layer depths and degree of fine-tuning on transfer learning with a deep convolutional neural network (CNN)-based COVID-19 screening in CXR to identify efficient transfer learning strategies. The CXR images used in this study were collected from publicly available repositories, and the collected images were classified into three classes: COVID-19, pneumonia, and normal. To evaluate the effect of layer depths of the same CNN architecture, CNNs called VGG-16 and VGG-19 were used as backbone networks. Then, each backbone network was trained with different degrees of fine-tuning and comparatively evaluated. The experimental results showed the highest AUC value to be 0.950 concerning COVID-19 classification in the experimental group of a fine-tuned with only 2/5 blocks of the VGG16 backbone network. In conclusion, in the classification of medical images with a limited number of data, a deeper layer depth may not guarantee better results. In addition, even if the same pre-trained CNN architecture is used, an appropriate degree of fine-tuning can help to build an efficient deep learning model.

scholarly journals Novel Method for Detection of Sars-COV2 Using Chest X-Ray Images

2021 ◽  
pp. 145-149
Author(s):  
Mrs Tejaswini ML ◽  
Ashwni H ◽  
Chandana N ◽  
Harshitha BR ◽  
Nagashree HN
Keyword(s):  
Public Health ◽  
Health Sector ◽  
Cost Effective ◽  
Fast Response ◽  
Training Data ◽  
X Ray ◽  
Public Areas ◽  
Normal Chest ◽  
Chest X Ray ◽  
Radiograph Image

A coronavirus have a great impact on a public health globally. Real time PCR s used for pathological testing but that result in false test result this impact made to exploration of alternate method for testing [1]. The detection of coronavirus 2 using chest X-ray image is anlifesaving property. By using chest X-ray coronavirus can identified are cost effective and its available on every public health sector rural clinic hospital. Deep learning –based chest radiograph classification (DL-CRC) frame are used to distinguish the COVID-19 cases and normal cases will high accuracy. The pre-trained image database used for large training sets to have pre- trained weights .The training data consisting covid chest X-ray image and normal chest X-ray image and fed into customized convolution neural network (CNN) model in DL-CRC wear masks in public areas is a major protection for people .The classification implies that it can efficiently detection COVID-19 from radiograph image for provide a reliable and fast response of COVID-19 infection in the lung.

scholarly journals Training deep learning algorithms with weakly labeled pneumonia chest X-ray data for COVID-19 detection

2020 ◽  
Author(s):  
Sivaramakrishnan Rajaraman ◽  
Sameer Antani
Keyword(s):  
Data Augmentation ◽  
Feature Space ◽  
Distinct Pattern ◽  
Viral Pneumonia ◽  
Training Data ◽  
Polymerase Chain Reaction Test ◽  
X Ray ◽  
Chest X Ray ◽  
Baseline Test ◽  
Chain Reaction Test

AbstractThe novel Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has caused a pandemic resulting in over 2.7 million infected individuals and over 190,000 deaths and growing. Respiratory disorders in COVID-19 caused by the virus commonly present as viral pneumonia-like opacities in chest X-ray images which are used as an adjunct to the reverse transcription-polymerase chain reaction test for confirmation and evaluating disease progression. The surge places high demand on medical services including radiology expertise. However, there is a dearth of sufficient training data for developing image-based automated decision support tools to alleviate radiological burden. We address this insufficiency by expanding training data distribution through use of weakly-labeled images pooled from publicly available CXR collections showing pneumonia-related opacities. We use the images in a stage-wise, strategic approach and train convolutional neural network-based algorithms to detect COVID-19 infections in CXRs. It is observed that weakly-labeled data augmentation improves performance with the baseline test data compared to non-augmented training by expanding the learned feature space to encompass variability in the unseen test distribution to enhance inter-class discrimination, reduce intra-class similarity and generalization error. Augmentation with COVID-19 CXRs from individual collections significantly improves performance compared to baseline non-augmented training and weakly-labeled augmentation toward detecting COVID-19 like viral pneumonia in the publicly available COVID-19 CXR collections. This underscores the fact that COVID-19 CXRs have a distinct pattern and hence distribution, unlike non-COVID-19 viral pneumonia and other infectious agents.

SARS n-CoV2-19 detection from chest x-ray images using deep neural networks

2020 ◽  
Vol 16 (5) ◽  
pp. 419-427 ◽  
Author(s):  
Mohammad Khalid Pandit ◽  
Shoaib Amin Banday
Keyword(s):  
Health Care Professionals ◽  
Low Cost ◽  
Fine Tuning ◽  
Medical Community ◽  
X Rays ◽  
Data Set ◽  
Content Type ◽  
X Ray ◽  
Chest X Ray ◽  
Novel Coronavirus

Purpose Novel coronavirus is fast spreading pathogen worldwide and is threatening billions of lives. SARS n-CoV2 is known to affect the lungs of the COVID-19 positive patients. Chest x-rays are the most widely used imaging technique for clinical diagnosis due to fast imaging time and low cost. The purpose of this study is to use deep learning technique for automatic detection of COVID-19 using chest x-rays. Design/methodology/approach The authors used a data set containing confirmed COVID-19 positive, common bacterial pneumonia and healthy cases (no infection). A collection of 1,428 x-ray images is used in this study. The authors used a pre-trained VGG-16 model for the classification task. Transfer learning with fine-tuning was used in this study to effectively train the network on a relatively small chest x-ray data set. Initial experiments show that the model achieves promising results and can be greatly used to expedite COVID-19 detection. Findings The authors achieved an accuracy of 96% and 92.5% in two and three output class cases, respectively. Based on these findings, the medical community can access using x-ray images as possible diagnostic tool for faster COVID-19 detection to complement the already testing and diagnosis methods. Originality/value The proposed method can be used as initial screening which can help health-care professionals to better treat the COVID patients by timely detecting and screening the presence of disease.

scholarly journals Evaluating Convolutional Neural Networks for COVID-19 classification in chest X-ray images

2020 ◽  
Author(s):  
Leonardo Rodrigues ◽  
Larissa Rodrigues ◽  
Danilo Da Silva ◽  
João Fernando Mari
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Data Augmentation ◽  
Relevant Information ◽  
Fine Tuning ◽  
Machine Learning Techniques ◽  
Disease Spread ◽  
X Ray ◽  
Chest X Ray ◽  
Scalable Methods

Coronavirus Disease 2019 (COVID-19) pandemic rapidly spread globally, impacting the lives of billions of people. The effective screening of infected patients is a critical step to struggle with COVID-19, and treating the patients avoiding this quickly disease spread. The need for automated and scalable methods has increased due to the unavailability of accurate automated toolkits. Recent researches using chest X-ray images suggest they include relevant information about the COVID-19 virus. Hence, applying machine learning techniques combined with radiological imaging promises to identify this disease accurately. It is straightforward to collect these images once it is spreadly shared and analyzed in the world. This paper presents a method for automatic COVID-19 detection using chest Xray images through four convolutional neural networks, namely: AlexNet, VGG-11, SqueezeNet, and DenseNet-121. This method had been providing accurate diagnostics for positive or negative COVID-19 classification. We validate our experiments using a ten-fold cross-validation procedure over the training and test sets. Our findings include the shallow fine-tuning and data augmentation strategies that can assist in dealing with the low number of positive COVID-19 images publicly available. The accuracy for all CNNs is higher than 97.00%, and the SqueezeNet model achieved the best result with 99.20%.

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