zurlog/subset_wlabels huggingface dataset:
Title: cvdnet: a novel deep learning architecture for detection of coronavirus (covid-19) from chest x-ray images
Journal: Chaos Solitons Fractals
Published: 2020-09-15 (Journal Article, Peer reviewed (PubMed))
Condition: COVID-19;COVID-19 Pandemic;Communicable Diseases;Infections;Pneumonia;Pneumonia, Viral
Task: 2. Detection/Diagnosis | Modality: X-Ray

Abstract: The COVID-19 pandemic is an emerging respiratory infectious disease, also known as coronavirus 2019. It appears in November 2019 in Hubei province (in China), and more specifically in the city of Wuhan, then spreads in the whole world. As the number of cases increases with unprecedented speed, many parts of the world are facing a shortage of resources and testing. Faced with this problem, physicians, scientists and engineers, including specialists in Artificial Intelligence (AI), have encouraged the development of a Deep Learning model to help healthcare professionals to detect COVID-19 from chest X-ray images and to determine the severity of the infection in a very short time, with low cost. In this paper, we propose CVDNet, a Deep Convolutional Neural Network (CNN) model to classify COVID-19 infection from normal and other pneumonia cases using chest X-ray images. The proposed architecture is based on the residual neural network and it is constructed by using two parallel levels with different kernel sizes to capture local and global features of the inputs. This model is trained on a dataset publically available containing a combination of 219 COVID-19, 1341 normal and 1345 viral pneumonia chest x-ray images. The experimental results reveal that our CVDNet. These results represent a promising classification performance on a small dataset which can be further achieve better results with more training data. Overall, our CVDNet model can be an interesting tool to help radiologists in the diagnosis and early detection of COVID-19 cases.
Title: use of ct and artificial intelligence in suspected or covid-19 positive patients: statement of the italian society of medical and interventional radiology
Journal: Radiol Med
Published: 2020-05-01 (Journal Article, Peer reviewed (PubMed))
Condition: COVID-19;COVID-19 Pandemic;Coronavirus Infections;Pneumonia
Task: Review | Modality: CT

Abstract: The COVID-19 pandemic started in Italy in February 2020 with an exponential growth that has exceeded the number of cases reported in China. Italian radiology departments found themselves at the forefront in the management of suspected and positive COVID cases, both in diagnosis, in estimating the severity of the disease and in follow-up. In this context SIRM recommends chest X-ray as first-line imaging tool, CT as additional tool that shows typical features of COVID pneumonia, and ultrasound of the lungs as monitoring tool. SIRM recommends, as high priority, to ensure appropriate sanitation procedures on the scan equipment after detecting any suspected or positive COVID-19 patients. In this emergency situation, several expectations have been raised by the scientific community about the role that artificial intelligence can have in improving the diagnosis and treatment of coronavirus infection, and SIRM wishes to deliver clear statements to the radiological community, on the usefulness of artificial intelligence as a radiological decision support system in COVID-19 positive patients. SIRM supports the research on the use of artificial intelligence as a predictive and prognostic decision support system, especially in hospitalized patients and those admitted to intensive care, and welcomes single center of multicenter studies for a clinical validation of the test. SIRM does not support the use of CT with artificial intelligence for screening or as first-line test to diagnose COVID-19. Chest CT with artificial intelligence cannot replace molecular diagnosis tests with nose-pharyngeal swab (rRT-PCR) in suspected for COVID-19 patients.
Title: transfer learning-based automatic detection of coronavirus disease 2019 (covid-19) from chest x-ray images
Journal: J Biomed Phys Eng
Published: 2020-11-03 (Journal Article, Peer reviewed (PubMed))
Condition: COVID-19;Communicable Diseases, Emerging;Infections
Task: 2. Detection/Diagnosis | Modality: X-Ray

Abstract: Coronavirus disease 2019 (COVID-19) is an emerging infectious disease and global health crisis. Although real-time reverse transcription polymerase chain reaction (RT-PCR) is known as the most widely laboratory method to detect the COVID-19 from respiratory specimens. It suffers from several main drawbacks such as time-consuming, high false-negative results, and limited availability. Therefore, the automatically detect of COVID-19 will be required. This study aimed to use an automated deep convolution neural network based pre-trained transfer models for detection of COVID-19 infection in chest X-rays. In a retrospective study, we have applied Visual Geometry Group (VGG)-16, VGG-19, MobileNet, and InceptionResNetV2 pre-trained models for detection COVID-19 infection from 348 chest X-ray images. Our proposed models have been trained and tested on a dataset which previously prepared. The all proposed models provide accuracy greater than 90.0%. The pre-trained MobileNet model provides the highest classification performance of automated COVID-19 classification with 99.1% accuracy in comparison with other three proposed models. The plotted area under curve (AUC) of receiver operating characteristics (ROC) of VGG16, VGG19, MobileNet, and InceptionResNetV2 models are 0.92, 0.91, 0.99, and 0.97, respectively. The all proposed models were able to perform binary classification with the accuracy more than 90.0% for COVID-19 diagnosis. Our data indicated that the MobileNet can be considered as a promising model to detect COVID-19 cases. In the future, by increasing the number of samples of COVID-19 chest X-rays to the training dataset, the accuracy and robustness of our proposed models increase further.
Title: covsegnet: a multi encoder-decoder architecture for improved lesion segmentation of covid-19 chest ct scans
Published: 2020-12-02 (Preprint, arXiv)
Condition: COVID-19
Task: Segmentation-only | Modality: CT

Abstract: Automatic lung lesions segmentation of chest CT scans is considered a pivotal stage towards accurate diagnosis and severity measurement of COVID-19. Traditional U-shaped encoder-decoder architecture and its variants suffer from diminutions of contextual information in pooling/upsampling operations with increased semantic gaps among encoded and decoded feature maps as well as instigate vanishing gradient problems for its sequential gradient propagation that result in sub-optimal performance. Moreover, operating with 3D CT-volume poses further limitations due to the exponential increase of computational complexity making the optimization difficult. In this paper, an automated COVID-19 lesion segmentation scheme is proposed utilizing a highly efficient neural network architecture, namely CovSegNet, to overcome these limitations. Additionally, a two-phase training scheme is introduced where a deeper 2D-network is employed for generating ROI-enhanced CT-volume followed by a shallower 3D-network for further enhancement with more contextual information without increasing computational burden. Along with the traditional vertical expansion of Unet, we have introduced horizontal expansion with multi-stage encoder-decoder modules for achieving optimum performance. Additionally, multi-scale feature maps are integrated into the scale transition process to overcome the loss of contextual information. Moreover, a multi-scale fusion module is introduced with a pyramid fusion scheme to reduce the semantic gaps between subsequent encoder/decoder modules while facilitating the parallel optimization for efficient gradient propagation. Outstanding performances have been achieved in three publicly available datasets that largely outperform other state-of-the-art approaches. The proposed scheme can be easily extended for achieving optimum segmentation performances in a wide variety of applications.
Title: automated assessment and tracking of covid-19 pulmonary disease severity on chest radiographs using convolutional siamese neural networks
Journal: Radiol Artif Intell
Published: 2021-05-01 (Journal Article, Peer reviewed (PubMed))
Condition: COVID-19;Death;Lung Diseases
Task: 3. Monitoring/Severity assessment | Modality: X-Ray

Abstract: To develop an automated measure of COVID-19 pulmonary disease severity on chest radiographs (CXRs), for longitudinal disease tracking and outcome prediction. A convolutional Siamese neural network-based algorithm was trained to output a measure of pulmonary disease severity on CXRs (pulmonary x-ray severity (PXS) score), using weakly-supervised pretraining on approx. 160,000 anterior-posterior images from CheXpert and transfer learning on 314 frontal CXRs from COVID-19 patients. The algorithm was evaluated on internal and external test sets from different hospitals (154 and 113 CXRs respectively). PXS scores were correlated with radiographic severity scores independently assigned by two thoracic radiologists and one in-training radiologist (Pearson r). For 92 internal test set patients with follow-up CXRs, PXS score change was compared to radiologist assessments of change (Spearman ρ). The association between PXS score and subsequent intubation or death was assessed. Bootstrap 95% confidence intervals (CI) were calculated. PXS scores correlated with radiographic pulmonary disease severity scores assigned to CXRs in the internal and external test sets (r=0.86 (95%CI 0.80-0.90) and r=0.86 (95%CI 0.79-0.90) respectively). The direction of change in PXS score in follow-up CXRs agreed with radiologist assessment (ρ=0.74 (95%CI 0.63-0.81)). In patients not intubated on the admission CXR, the PXS score predicted subsequent intubation or death within three days of hospital admission (area under the receiver operating characteristic curve=0.80 (95%CI 0.75-0.85)). A Siamese neural network-based severity score automatically measures radiographic COVID-19 pulmonary disease severity, which can be used to track disease change and predict subsequent intubation or death.
Title: deep convolutional neural networks to diagnose covid-19 and other pneumonia diseases from posteroanterior chest x-rays
Published: 2020-05-02 (Preprint, arXiv)
Condition: COVID-19;Infections;Pneumonia
Task: 2. Detection/Diagnosis | Modality: X-Ray

Abstract: The article explores different deep convolutional neural network architectures trained and tested on posteroanterior chest X-rays of 327 patients who are healthy (152 patients), diagnosed with COVID-19 , and other types of pneumonia . In particular, this paper looks at the deep convolutional neural networks VGG16 and VGG19, InceptionResNetV2 and InceptionV3, as well as Xception, all followed by a flat multi-layer perceptron and a final 30% drop-out. The paper has found that the best performing network is VGG16 with a final 30% drop-out trained over 3 classes (COVID-19, No Finding, Other Pneumonia). It has an internal cross-validated accuracy of 93.9%, a COVID-19 sensitivity of 87.7%, and a No Finding sensitivity of 96.8%. The respective external cross-validated values are 84.1%, and 96.8%. The model optimizer was Adam with a 1e-4 learning rate, and categorical cross-entropy loss. It is hoped that, once this research will be put to practice in hospitals, healthcare professionals will be able in the medium to long-term to diagnosing through machine learning tools possible pneumonia, and if detected, whether it is linked to a COVID-19 infection, allowing the detection of new possible COVID-19 foyers after the end of possible "stop-and-go" lockdowns as expected by until a vaccine is found and widespread. Furthermore, in the short-term, it is hoped practitioners can compare the diagnosis from the deep convolutional neural networks with possible RT-PCR testing results, and if clashing, a Computed Tomography could be performed as they are more accurate in showing COVID-19 pneumonia.
Title: ct image segmentation for inflamed and fibrotic lungs using a multi-resolution convolutional neural network
Journal: Sci Rep
Published: 2021-01-16 (Journal Article;Research Support, N.I.H., Extramural;Research Support, Non-U.S. Gov't;Research Support, U.S. Gov't, Non-P.H.S., Peer reviewed (PubMed))
Condition: Acute Lung Injury;COVID-19;Lung Cancer;Pulmonary Disease, Chronic Obstructive
Task: Segmentation-only | Modality: CT

Abstract: The purpose of this study was to develop a fully-automated segmentation algorithm, robust to various density enhancing lung abnormalities, to facilitate rapid quantitative analysis of computed tomography images. A polymorphic training approach is proposed, in which both specifically labeled left and right lungs of humans with COPD, and nonspecifically labeled lungs of animals with acute lung injury, were incorporated into training a single neural network. The resulting network is intended for predicting left and right lung regions in humans with or without diffuse opacification and consolidation. Performance of the proposed lung segmentation algorithm was extensively evaluated on CT scans of subjects with COPD, confirmed COVID-19, lung cancer, and IPF, despite no labeled training data of the latter three diseases. Lobar segmentations were obtained using the left and right lung segmentation as input to the LobeNet algorithm. Regional lobar analysis was performed using hierarchical clustering to identify radiographic subtypes of COVID-19. The proposed lung segmentation algorithm was quantitatively evaluated using semi-automated and manually-corrected segmentations in 87 COVID-19 CT images, achieving an average symmetric surface distance of mm and Dice coefficient of . Hierarchical clustering identified four radiographical phenotypes of COVID-19 based on lobar fractions of consolidated and poorly aerated tissue. Lower left and lower right lobes were consistently more afflicted with poor aeration and consolidation. However, the most severe cases demonstrated involvement of all lobes. The polymorphic training approach was able to accurately segment COVID-19 cases with diffuse consolidation without requiring COVID-19 cases for training.
Title: deep transfer learning for covid-19 prediction: case study for limited data problems
Journal: Curr Med Imaging
Published: 2020-11-25 (Journal Article;Research Support, Non-U.S. Gov't, Peer reviewed (PubMed))
Condition: COVID-19
Task: 2. Detection/Diagnosis | Modality: X-Ray

Abstract: Automatic prediction of COVID-19 using deep convolution neural networks based pre-trained transfer models and Chest X-ray images. This research employs the advantages of computer vision and medical image analysis to develop an automated model that has the clinical potential for early detection of the disease. Using Deep Learning models, the research aims at evaluating the effectiveness and accuracy of different convolutional neural networks models in the automatic diagnosis of COVID-19 from X-ray images as compared to diagnosis performed by experts in the medical community. Due to the fact that the dataset available for COVID-19 is still limited, the best model to use is the InceptionNetV3. Performance results show that the InceptionNetV3 model yielded the highest accuracy of 98.63% (with data augmentation) and 98.90% (without data augmentation) among the three models designed. However, as the dataset gets bigger, the Inception ResNetV2 and NASNetlarge will do a better job of classification. All the performed networks tend to over-fit when data augmentation is not used, this is due to the small amount of data used for training and validation. A deep transfer learning is proposed to detecting the COVID-19 automatically from chest X-ray by training it with X-ray images gotten from both COVID-19 patients and people with normal chest X-rays. The study is aimed at helping doctors in making decisions in their clinical practice due its high performance and effectiveness, the study also gives an insight to how transfer learning was used to automatically detect the COVID-19.
Title: classification and region analysis of covid-19 infection using lung ct images and deep convolutional neural networks
Published: 2020-09-15 (Preprint, arXiv)
Condition: COVID-19;Infections
Task: 2. Detection/Diagnosis | Modality: CT

Abstract: COVID-19 is a global health problem. Consequently, early detection and analysis of the infection patterns are crucial for controlling infection spread as well as devising a treatment plan. This work proposes a two-stage deep Convolutional Neural Networks (CNNs) based framework for delineation of COVID-19 infected regions in Lung CT images. In the first stage, initially, COVID-19 specific CT image features are enhanced using a two-level discrete wavelet transformation. These enhanced CT images are then classified using the proposed custom-made deep CoV-CTNet. In the second stage, the CT images classified as infectious images are provided to the segmentation models for the identification and analysis of COVID-19 infectious regions. In this regard, we propose a novel semantic segmentation model CoV-RASeg, which systematically uses average and max pooling operations in the encoder and decoder blocks. This systematic utilization of max and average pooling operations helps the proposed CoV-RASeg in simultaneously learning both the boundaries and region homogeneity. Moreover, the idea of attention is incorporated to deal with mildly infected regions. The proposed two-stage framework is evaluated on a standard Lung CT image dataset, and its performance is compared with the existing deep CNN models. The performance of the proposed CoV-CTNet is evaluated using Mathew Correlation Coefficient (MCC) measure and that of proposed CoV-RASeg using Dice Similarity (DS) score . The promising results on an unseen test set suggest that the proposed framework has the potential to help the radiologists in the identification and analysis of COVID-19 infected regions.
Title: deep learning analysis provides accurate covid-19 diagnosis on chest computed tomography
Journal: Eur J Radiol
Published: 2020-11-16 (Journal Article, Peer reviewed (PubMed))
Condition: COVID-19
Task: 2. Detection/Diagnosis | Modality: CT

Abstract: Computed Tomography is an essential diagnostic tool in the management of COVID-19. Considering the large amount of examinations in high case-load scenarios, an automated tool could facilitate and save critical time in the diagnosis and risk stratification of the disease. A novel deep learning derived machine learning (ML) classifier was developed using a simplified programming approach and an open source dataset consisting of 6868 chest CT images from 418 patients which was split into training and validation subsets. The diagnostic performance was then evaluated and compared to experienced radiologists on an independent testing dataset. Diagnostic performance metrics were calculated using Receiver Operating Characteristics (ROC) analysis. Operating points with high positive and low negative likelihood ratios to stratify the risk of COVID-19 being present were identified and validated. The model achieved an overall accuracy of 0.956 (AUC) on an independent testing dataset of 90 patients. Both rule-in and rule out thresholds were identified and tested. At the rule-in operating point, sensitivity and specificity were 84.4 % and 93.3 % and did not differ from both radiologists (p > 0.05). At the rule-out threshold, sensitivity and specificity differed significantly from the radiologists (p < 0.05). Likelihood ratios and a Fagan nomogram provide prevalence independent test performance estimates. Accurate diagnosis of COVID-19 using a basic deep learning approach is feasible using open-source CT image data. In addition, the machine learning classifier provided validated rule-in and rule-out criteria could be used to stratify the risk of COVID-19 being present.
Title: blockchain-federated-learning and deep learning models for covid-19 detection using ct imaging
Journal: IEEE Sens J
Published: 2021-04-30 (Journal Article, Peer reviewed (PubMed))
Condition: COVID-19
Task: 2. Detection/Diagnosis | Modality: CT

Abstract: With the increase of COVID-19 cases worldwide, an effective way is required to diagnose COVID-19 patients. The primary problem in diagnosing COVID-19 patients is the shortage and reliability of testing kits, due to the quick spread of the virus, medical practitioners are facing difficulty in identifying the positive cases. The second real-world problem is to share the data among the hospitals globally while keeping in view the privacy concerns of the organizations. Building a collaborative model and preserving privacy are the major concerns for training a global deep learning model. This paper proposes a framework that collects a small amount of data from different sources (various hospitals) and trains a global deep learning model using blockchain-based federated learning. Blockchain technology authenticates the data and federated learning trains the model globally while preserving the privacy of the organization. First, we propose a data normalization technique that deals with the heterogeneity of data as the data is gathered from different hospitals having different kinds of Computed Tomography (CT) scanners. Secondly, we use Capsule Network-based segmentation and classification to detect COVID-19 patients. Thirdly, we design a method that can collaboratively train a global model using blockchain technology with federated learning while preserving privacy. Additionally, we collected real-life COVID-19 patients' data open to the research community. The proposed framework can utilize up-to-date data which improves the recognition of CT images. Finally, we conducted comprehensive experiments to validate the proposed method. Our results demonstrate better performance for detecting COVID-19 patients.
Title: implementation of convolutional neural network approach for covid-19 disease detection
Journal: Physiol Genomics
Published: 2020-10-24 (Journal Article, Peer reviewed (PubMed))
Condition: COVID-19;Pneumonia
Task: 2. Detection/Diagnosis | Modality: X-Ray

Abstract: In this paper, two novel, powerful, and robust convolutional neural network (CNN) architectures are designed and proposed for two different classification tasks using publicly available data sets. The first architecture is able to decide whether a given chest X-ray image of a patient contains COVID-19 or not with 98.92% average accuracy. The second CNN architecture is able to divide a given chest X-ray image of a patient into three classes (COVID-19 versus normal versus pneumonia) with 98.27% average accuracy. The hyperparameters of both CNN models are automatically determined using Grid Search. Experimental results on large clinical data sets show the effectiveness of the proposed architectures and demonstrate that the proposed algorithms can overcome the disadvantages mentioned above. Moreover, the proposed CNN models are fully automatic in terms of not requiring the extraction of diseased tissue, which is a great improvement of available automatic methods in the literature. To the best of the author's knowledge, this study is the first study to detect COVID-19 disease from given chest X-ray images, using CNN, whose hyperparameters are automatically determined by the Grid Search. Another important contribution of this study is that it is the first CNN-based COVID-19 chest X-ray image classification study that uses the largest possible clinical data set. A total of 1,524 COVID-19, 1,527 pneumonia, and 1524 normal X-ray images are collected. It is aimed to collect the largest number of COVID-19 X-ray images that exist in the literature until the writing of this research paper.