COVID-19 in CXR: From Detection and Severity Scoring to Patient Disease Monitoring

Frid-Adar, Maayan; Amer, Rula; Gozes, Ophir; Nassar, Jannette; Greenspan, Hayit

doi:10.1109/jbhi.2021.3069169

Cited by 48 publications

(27 citation statements)

References 38 publications

(74 reference statements)

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“…The authors claimed an accuracy of 89.41%, 85.96% and 98.11% respectively on all three datasets utilized. Similarly, authors in [39]- [41] have proposed methodologies for COVID-19 detection using deep learning, transfer learning and F-transform techniques relatively to detect and binary classify the input image as healthy or infected. The authors have used RSNA pneumonia detection dataset, Covid-19 radiography and COVID-19 datasets respectively to perform their COVID-19 research and claimed promising results.…”

Section: A Classification Based On Entire Input Imagementioning

confidence: 99%

“…The authors have used RSNA pneumonia detection dataset, Covid-19 radiography and COVID-19 datasets respectively to perform their COVID-19 research and claimed promising results. For instance, [39] claimed 86% accuracy and 93% AUC values, [40] claimed 98% accuracy and 99% AUC values. Whereas, [41] claimed 86% accuracy values.…”

Section: A Classification Based On Entire Input Imagementioning

confidence: 99%

See 1 more Smart Citation

Lung Segmentation-Based Pulmonary Disease Classification Using Deep Neural Networks

et al. 2021

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Interpreting chest x-ray (CXR) to find anomalies in the thoracic region is a tedious job and can consume an ample amount of radiologist's time when there are thousands of them to process. In such scenarios, the Computer-Aided Diagnostic (CAD) systems can help radiologists by doing the trivial processing and presenting the information in a meaningful way so that, the radiologist can make more accurate decisions by spending less amount of time and energy. This research study intends to propose a better, accurate, and efficient CNN based pulmonary disease diagnosis system using CXR images. In the proposed system, the capabilities of deep neural network architecture are exploited by proposing a custom CNN architecture with additional layers and modified hyperparameters to meet the required results. The input CXR is examined for healthy or infected at the surface level and the infected images are further processed for class level label classification. The lung region is segmented from the entire input CXR image to reduce the amount of noise and increase the processing efficiency by processing less overall information. The proposed model is evaluated on the benchmark split of the NIH chest x-ray dataset and achieves better segmentation and classification results when compared to the state of the art approaches.

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Section: A Classification Based On Entire Input Imagementioning

confidence: 99%

Section: A Classification Based On Entire Input Imagementioning

confidence: 99%

Lung Segmentation-Based Pulmonary Disease Classification Using Deep Neural Networks

et al. 2021

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“…A linear regression model was then trained on severity scores for pneumonia extent and opacity scores provided by three expert radiologists. Amer et al [ 15 ] trained a deep learning model to simultaneously train a detection and localization model for pneumonia in CXRs. The localization maps were then used to estimate a pneumonia ratio indicating severity of infection.…”

Section: Introductionmentioning

confidence: 99%

Disease Progression Detection via Deep Sequence Learning of Successive Radiographic Scans

Ahmad

Saudagar

Malik

et al. 2022

IJERPH

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The highly rapid spread of the current pandemic has quickly overwhelmed hospitals all over the world and motivated extensive research to address a wide range of emerging problems. The unforeseen influx of COVID-19 patients to hospitals has made it inevitable to deploy a rapid and accurate triage system, monitor progression, and predict patients at higher risk of deterioration in order to make informed decisions regarding hospital resource management. Disease detection in radiographic scans, severity estimation, and progression and prognosis prediction have been extensively studied with the help of end-to-end methods based on deep learning. The majority of recent works have utilized a single scan to determine severity or predict progression of the disease. In this paper, we present a method based on deep sequence learning to predict improvement or deterioration in successive chest X-ray scans and build a mathematical model to determine individual patient disease progression profile using successive scans. A deep convolutional neural network pretrained on a diverse lung disease dataset was used as a feature extractor to generate the sequences. We devised three strategies for sequence modeling in order to obtain both fine-grained and coarse-grained features and construct sequences of different lengths. We also devised a strategy to quantify positive or negative change in successive scans, which was then combined with age-related risk factors to construct disease progression profile for COVID-19 patients. The age-related risk factors allowed us to model rapid deterioration and slower recovery in older patients. Experiments conducted on two large datasets showed that the proposed method could accurately predict disease progression. With the best feature extractor, the proposed method was able to achieve AUC of 0.98 with the features obtained from radiographs. Furthermore, the proposed patient profiling method accurately estimated the health profile of patients.

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“…They collected review studies that diagnosed COVID-19 by predicting the severity using ML models. A disease severity has been calculated as pneumonia ratio in [11] using Chest X-Ray images and also reported the longitudinal study reports on progression of infections on the patients. In [7] , the huge number of chest X-Ray images were augmented and fed into pre-trained model trained with Imagenet.…”

Section: Introductionmentioning

confidence: 99%

Prediction of COVID Criticality Score with Laboratory, Clinical and CT Images using Hybrid Regression Models

Varalakshmi

Narayanan

Rajasekar

2021

Computer Methods and Programs in Biomedicine

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Background and Objective: Rapid and precise diagnosis of COVID-19 is very critical in hotspot regions. The main aim of this proposed work is to investigate the baseline, laboratory and CT features of COVID-19 affected patients of two groups (Early and Critical stages). The detection model for COVID-19 is built depending upon the manifestations that define the severity of the disease. Methods: The CT scan images are fed into the various deep learning, machine learning and hybrid learning models to mine the necessary features and predict CT Score. The predicted CT score along with other clinical, laboratory and CT scan image features are then passed to train the various Regression models for predicting the COVID Criticality (CC) Score. These baseline, laboratory and CT features of COVID-19 are reduced using Statistical analysis and Univariate logistic regression analysis. Results: When analysing the prediction of CT scores using images alone, AlexNet+Lasso yields better outcome with regression score of 0.9643 and RMSE of 0.0023 when compared with Decision tree (RMSE of 0.0034; Regression score of 0.9578) and GRU (RMSE of 0.1253; regression score of 0.9323). When analysing the prediction of CC scores using CT scores and other baseline, laboratory and CT features, VGG-16+Linear Regression yields better results with regression score of 0.9911 and RMSE of 0.0002 when compared with Linear SVR (RMSE of 0.0006; Regression score of 0.9911) and LSTM (RMSE of 0.0005; Regression score of 0.9877). The correlation analysis is performed to identify the significance of utilizing other features in prediction of CC Score. The correlation coefficient of CT scores with actual value is 0.93 and 0.92 for Early stage group and Critical stage group respectively. The correlation coefficient of CC scores with actual value is 0.96 for Early stage group and 0.95 for Critical stage group.The classification of COVID-19 patients are carried out with the help of predicted CC Scores. Conclusions: This proposed work is carried out in the motive of helping radiologists in faster categorization of COVID patients as Early or Severe staged using CC Scores. The automated prediction of COVID Criticality Score using our diagnostic model can help radiologists and physicians save time for carrying out further treatment and procedures.

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COVID-19 in CXR: From Detection and Severity Scoring to Patient Disease Monitoring

Cited by 48 publications

References 38 publications

Lung Segmentation-Based Pulmonary Disease Classification Using Deep Neural Networks

Lung Segmentation-Based Pulmonary Disease Classification Using Deep Neural Networks

Disease Progression Detection via Deep Sequence Learning of Successive Radiographic Scans

Prediction of COVID Criticality Score with Laboratory, Clinical and CT Images using Hybrid Regression Models

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