Bi-Level Prediction Model for Screening COVID-19 Patients Using Chest X-Ray Images

Das, Sagnik; Roy, Subhasis; Malakar, Samir; Velásquez, Juan D.; Sarkar, Ram

doi:10.1016/j.bdr.2021.100233

Cited by 28 publications

(22 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, the COVID-19 data used in this paper is limited. Recent papers such as Das et al [24] show that there are more COVID-19 data publicly available. Hence, if we could collect more data, the experimental results may be different.…”

Section: Discussionmentioning

confidence: 99%

Section: Existing Modelsmentioning

confidence: 99%

“…Furthermore, in addition to the baseline model by Lee et al [11], we compare the performance of our proposed systems through two recently released models by Das et al [24] and Rahimzadeh et al [25]. Das et al [24] proposed a two-stage machine learning model for classifying COVID-19 using CXR images. The proposed model extracts CXR features with the VGG19 model pretrained with ImageNet dataset, and classifies CXR images into normal and abnormal by logistic regression.…”

Section: Existing Modelsmentioning

confidence: 99%

“…Accuracy, sensitivity, specificity, AUC and F1 score of the VGG-16 with fine-tuning by Lee et al [11], bi-level classifier by Das et al [24] and Xception and ResNet50V2 concated model by Rahimzadeh et al [25] were set for comparison with our proposed method. Besides, we compared our proposed method with other well-known models, such as ResNet-50, ResNet-101 [29], MobileNet [30], and MobileNetV2 [31] which were utilized in other COVID-19 research [32][33][34].…”

Section: Existing Modelsmentioning

confidence: 99%

See 3 more Smart Citations

A Deep Learning Model with Self-Supervised Learning and Attention Mechanism for COVID-19 Diagnosis Using Chest X-ray Images

2021

View full text Add to dashboard Cite

The SARS-CoV-2 virus has spread worldwide, and the World Health Organization has declared COVID-19 pandemic, proclaiming that the entire world must overcome it together. The chest X-ray and computed tomography datasets of individuals with COVID-19 remain limited, which can cause lower performance of deep learning model. In this study, we developed a model for the diagnosis of COVID-19 by solving the classification problem using a self-supervised learning technique with a convolution attention module. Self-supervised learning using a U-shaped convolutional neural network model combined with a convolution block attention module (CBAM) using over 100,000 chest X-Ray images with structure similarity (SSIM) index captures image representations extremely well. The system we proposed consists of fine-tuning the weights of the encoder after a self-supervised learning pretext task, interpreting the chest X-ray representation in the encoder using convolutional layers, and diagnosing the chest X-ray image as the classification model. Additionally, considering the CBAM further improves the averaged accuracy of 98.6%, thereby outperforming the baseline model (97.8%) by 0.8%. The proposed model classifies the three classes of normal, pneumonia, and COVID-19 extremely accurately, along with other metrics such as specificity and sensitivity that are similar to accuracy. The average area under the curve (AUC) is 0.994 in the COVID-19 class, indicating that our proposed model exhibits outstanding classification performance.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Existing Modelsmentioning

confidence: 99%

Section: Existing Modelsmentioning

confidence: 99%

Section: Existing Modelsmentioning

confidence: 99%

See 2 more Smart Citations

A Deep Learning Model with Self-Supervised Learning and Attention Mechanism for COVID-19 Diagnosis Using Chest X-ray Images

2021

View full text Add to dashboard Cite

show abstract

“…Moreover, a computer-aided diagnostic combined approach based on graph CNN and pre-trained CNN model (Kumar et al [19]), a deep Covix-net model (Vinod et al [20]), new deep hybrid and deep boosted hybrid learning models (Khan et al [21]), and a gradient weighted class activation mapping technique (Panwar et al [22]) for coronavirus detection exhibited accuracies of 97.60%, 97%, 98.53%, and 95%, respectively. Likewise, a DenseNet-201 architecture reported by Alhudhaif et al [23], a PSO-based eXtreme Gradient Boosting model recommended by Dias Júnior et al [24], an automatic AI-based system using majority voting ensemble techniques suggested by Chandra et al [25], and a bi-level prediction model by Das et al [26] exhibited respective accuracies of 94.96%,98.71%, 91.329%, and96.74%to diagnose COVID-19. Other novel techniques to detect coronavirus include a deep LSTM model (Demir et al [27]), Inception-v3 model based on deep CNN associated with Multi-Layered Perceptron model called CovScanNet (Sait et al [28]), and a hybrid deep CNN technique with discrete wavelet transform features (Mostafiz et al [29].…”

Section: Introductionmentioning

confidence: 99%

COVID-19 Patient Detection Based on Fusion of Transfer Learning and Fuzzy Ensemble Models Using CXR Images

2021

View full text Add to dashboard Cite

The COVID-19 pandemic has claimed the lives of millions of people and put a significant strain on healthcare facilities. To combat this disease, it is necessary to monitor affected patients in a timely and cost-effective manner. In this work, CXR images were used to identify COVID-19 patients. We compiled a CXR dataset with equal number of 2313 COVID positive, pneumonia and normal CXR images and utilized various transfer learning models as base classifiers, including VGG16, GoogleNet, and Xception. The proposed methodology combines fuzzy ensemble techniques, such as Majority Voting, Sugeno Integral, and Choquet Fuzzy, and adaptively combines the decision scores of the transfer learning models to identify coronavirus infection from CXR images. The proposed fuzzy ensemble methods outperformed each individual transfer learning technique and several state-of-the-art ensemble techniques in terms of accuracy and prediction. Specifically, VGG16 + Choquet Fuzzy, GoogleNet + Choquet Fuzzy, and Xception + Choquet Fuzzy achieved accuracies of 97.04%, 98.48%, and 99.57%, respectively. The results of this work are intended to help medical practitioners achieve an earlier detection of coronavirus compared to other detection strategies, which can further save millions of lives and advantageously influence society.

show abstract

Artificial Intelligence for the Future of Medicine

Ruiz

Velásquez²

2022

Intelligent Systems Reference Library

View full text Add to dashboard Cite

Bi-Level Prediction Model for Screening COVID-19 Patients Using Chest X-Ray Images

Cited by 28 publications

References 27 publications

A Deep Learning Model with Self-Supervised Learning and Attention Mechanism for COVID-19 Diagnosis Using Chest X-ray Images

A Deep Learning Model with Self-Supervised Learning and Attention Mechanism for COVID-19 Diagnosis Using Chest X-ray Images

COVID-19 Patient Detection Based on Fusion of Transfer Learning and Fuzzy Ensemble Models Using CXR Images

Artificial Intelligence for the Future of Medicine

Contact Info

Product

Resources

About