COVID-CXNet: Detecting COVID-19 in frontal chest X-ray images using deep learning

Haghanifar, Arman; Majdabadi, Mahdiyar Molahasani; Choi, Younhee; Deivalakshmi, S.; Ko, Seok‐Bum

doi:10.1007/s11042-022-12156-z

Cited by 99 publications

(40 citation statements)

References 44 publications

(20 reference statements)

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“…Following the work of a group of researchers from Qatar University and the University of Dhaka, Bangladesh, and collaborators from Pakistan, Malaysia, and medical doctors [ 77 , 78 ], we collected a dataset containing 3616 COVID-19 positives, 10,192 normal and 1345 viral pneumonia chest X-ray images. The COVID-19 data were collected from the various publicly accessible datasets, online sources, and published papers [ 79 , 80 , 81 , 82 , 83 , 84 ], normal data were collected from two different datasets [ 85 , 86 ], and viral pneumonia data were collected from chest X-ray images (pneumonia) database [ 86 ]. Few samples of chest X-ray images are shown in Figure 6 .…”

Section: Methodsmentioning

confidence: 99%

FedSGDCOVID: Federated SGD COVID-19 Detection under Local Differential Privacy Using Chest X-ray Images and Symptom Information

Tran

Huang

2022

Sensors

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Coronavirus (COVID-19) has created an unprecedented global crisis because of its detrimental effect on the global economy and health. COVID-19 cases have been rapidly increasing, with no sign of stopping. As a result, test kits and accurate detection models are in short supply. Early identification of COVID-19 patients will help decrease the infection rate. Thus, developing an automatic algorithm that enables the early detection of COVID-19 is essential. Moreover, patient data are sensitive, and they must be protected to prevent malicious attackers from revealing information through model updates and reconstruction. In this study, we presented a higher privacy-preserving federated learning system for COVID-19 detection without sharing data among data owners. First, we constructed a federated learning system using chest X-ray images and symptom information. The purpose is to develop a decentralized model across multiple hospitals without sharing data. We found that adding the spatial pyramid pooling to a 2D convolutional neural network improves the accuracy of chest X-ray images. Second, we explored that the accuracy of federated learning for COVID-19 identification reduces significantly for non-independent and identically distributed (Non-IID) data. We then proposed a strategy to improve the model’s accuracy on Non-IID data by increasing the total number of clients, parallelism (client-fraction), and computation per client. Finally, for our federated learning model, we applied a differential privacy stochastic gradient descent (DP-SGD) to improve the privacy of patient data. We also proposed a strategy to maintain the robustness of federated learning to ensure the security and accuracy of the model.

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Section: Methodsmentioning

confidence: 99%

FedSGDCOVID: Federated SGD COVID-19 Detection under Local Differential Privacy Using Chest X-ray Images and Symptom Information

Tran

Huang

2022

Sensors

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“…Haghanifar et al 25 made a hierarchical deep learning model for detecting Covid‐19. In the first level, images of chest X‐rays are classified into normal and pneumonia.…”

Section: Methodsmentioning

confidence: 99%

“…The data set used by the authors contains 780 Covid‐19‐positive X‐rays, 4600 X‐rays having CAP, and 5000 normal X‐rays. The approach taken by Haghanifar et al 25 was very similar to that of Rajpurkar et al 6 The key difference was that Haghanifar et al 25 first segmented the lungs from chest X‐ray and then they only used the part surrounding those lungs for classification. This approach, to a significant extent, solved the issue of “learning the wrong features to reach the right answer,” because then, the model was forced to learn only from the lung region rather than learning from the entire X‐ray, which usually contains a lot of regions other than the lungs.…”

Section: Methodsmentioning

confidence: 99%

Advancement of deep learning in pneumonia/Covid‐19 classification and localization: A systematic review with qualitative and quantitative analysis

Shah

2022

Chronic Diseases and Translational Medicine

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Around 450 million people are affected by pneumonia every year, which results in 2.5 million deaths. Coronavirus disease 2019 (Covid‐19) has also affected 181 million people, which led to 3.92 million casualties. The chances of death in both of these diseases can be significantly reduced if they are diagnosed early. However, the current methods of diagnosing pneumonia (complaints + chest X‐ray) and Covid‐19 (real‐time polymerase chain reaction) require the presence of expert radiologists and time, respectively. With the help of deep learning models, pneumonia and Covid‐19 can be detected instantly from chest X‐rays or computerized tomography (CT) scans. The process of diagnosing pneumonia/Covid‐19 can become faster and more widespread. In this paper, we aimed to elicit, explain, and evaluate qualitatively and quantitatively all advancements in deep learning methods aimed at detecting community‐acquired pneumonia, viral pneumonia, and Covid‐19 from images of chest X‐rays and CT scans. Being a systematic review, the focus of this paper lies in explaining various deep learning model architectures, which have either been modified or created from scratch for the task at hand. For each model, this paper answers the question of why the model is designed the way it is, the challenges that a particular model overcomes, and the tradeoffs that come with modifying a model to the required specifications. A grouped quantitative analysis of all models described in the paper is also provided to quantify the effectiveness of different models with a similar goal. Some tradeoffs cannot be quantified and, hence, they are mentioned explicitly in the qualitative analysis, which is done throughout the paper. By compiling and analyzing a large quantum of research details in one place with all the data sets, model architectures, and results, we aimed to provide a one‐stop solution to beginners and current researchers interested in this field.

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“…A hybrid model combining CNN with long short-term memory (LSTM) for the analysis of hybrid data (CT + CXR) has been proposed by Irfan et al 58 which showed an accuracy of 99% in detection of COVID and healthy classes. Al-Waisy et al 59 9 Combination of CNN and LSTM, 52 DarkCovidNet, 51 CoroNet based on Xception, 49 COVID-CXNet, 48 Truncated Inception Net, 11 different DCNNs + SVM, 67 and texture features + DCNNs (VGG19, ResNet50, and InceptionV3) 68 have been developed for the categorization of CXRs into three classes as normal or no findings, COVID, and pneumonia (viral and/ or bacterial). Among them, DarkCovidNet, CoroNet, COVID-CXNet, and Truncated Inception Net showed performance accuracies of 87.02%, 95%, 87.21%, and 99.96%, respectively, for the automated detection of normal, COVID, and pneumonia CXRs.…”

Section: Two-class Categorizationmentioning

confidence: 99%

VEntNet: Hybrid deep convolutional neural network model for automated multi‐class categorization of chest X‐rays

Sudarshan

Ramachandra²,

Tan

et al. 2022

Int J Imaging Syst Tech

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Visual interpretation of chest X‐rays (CXRs) is tedious and prone to error. Significant amount of time is spent by the radiologist in differentiating normal from abnormal CXRs and in identifying the location and type of abnormalities. An assistance tool for automatically classifying normal and different types of abnormal CXRs can facilitate the diagnosis and potentially save time costs. In this paper, a novel hybrid model having concatenation of Visual Geometry Group (VGG19) network and Entropy features as a modified deep convolutional neural network (DCNN) architecture, called VEntNet, is proposed for the automated multi‐class categorization of CXR images into normal, coronavirus disease (COVID), tuberculosis (TB), viral pneumonia, and bacterial pneumonia. The VEntNet model implemented consists of deep features extraction from convolutional layers of VGG19 network which are then concatenated with hand‐crafted entropy features extracted from CXRs. The concatenated features are then fed to the fully connected (FC) layers for performing multi‐class categorization using Softmax activation function. The performance of proposed VEntNet model is compared with other DCNNs with and without the hybrid approach for categorization of closely related lung pathologies and normal CXR images. Our proposed VEntNet achieved accuracies of 98.78% and 90.96%, respectively, for four and five‐class classification of CXRs. Thus, it is demonstrated that among the different DCNNs, our VEntNet outperformed in four‐class CXR categorization tasks. The proposed model can potentially save time by facilitating the screening of CXRs to identify those with abnormalities present as well as to categorize the abnormalities.

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COVID-CXNet: Detecting COVID-19 in frontal chest X-ray images using deep learning

Cited by 99 publications

References 44 publications

FedSGDCOVID: Federated SGD COVID-19 Detection under Local Differential Privacy Using Chest X-ray Images and Symptom Information

FedSGDCOVID: Federated SGD COVID-19 Detection under Local Differential Privacy Using Chest X-ray Images and Symptom Information

Advancement of deep learning in pneumonia/Covid‐19 classification and localization: A systematic review with qualitative and quantitative analysis

VEntNet: Hybrid deep convolutional neural network model for automated multi‐class categorization of chest X‐rays

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