An Uncertainty-Aware Transfer Learning-Based Framework for COVID-19 Diagnosis

Shamsi, Afshar; Asgharnezhad, Hamzeh; Jokandan, Shirin Shamsi; Khosravi, Abbas; Kebria, Parham M.; Nahavandi, Darius; Nahavandi, Saeid; Srinivasan, Dipti

doi:10.1109/tnnls.2021.3054306

Cited by 131 publications

(76 citation statements)

References 21 publications

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“…Further, the performance for each model on different test cases (covid19, Lung opacity, normal, viral Pneumonia) are shown in Table 3. The respective confusion matrices are also shown in Figure 8, Figure 9, and Figure 10.The best result for classifying covid19 was achieved with resnet-18 and densenet-121 with 100% accuracy which is 12.1% more than that presented in [7]. Also, as shown in Figure 7, the detection of the affected area is optimal using Resnet-18 and Densenet-121.…”

Section: Results and Observationsmentioning

confidence: 87%

“…Due to the above cited success and scope of deep learning based framework on chest x-ray image analysis, recently it's usage has shown significant growth on COVID19's patients chest x-rays analysis [5,6]. In [7], the authors have proposed a deep uncertainty-aware transfer learning based framework using four CNN models, namely, VGG16, DenseNet121, ResNet50, and Inception-ResNetV2 for COVID19 prediction and detection. The extracted features by CNN models are then used for multiple classification techniques.…”

Section: Related Workmentioning

confidence: 99%

“…Further, these approaches can not identify the affected area in the lungs. In this context, imaging technique processed through deep learning models are a better substitute for above mentioned diagnostic methods [7].…”

Section: Introductionmentioning

confidence: 99%

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Prediction and Detection of COVID-19 from Chest X-Rays using Transfer Learning based Deep Convolutional Neural Networks

Misra

Panigrahi

2021

Preprint

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With the ongoing outbreak of the COVID-19 global pandemic, the research community still struggles to develop early and reliable prediction and detection mechanisms for this infectious disease. The commonly used RT-PCR test is not readily available in areas with limited testing facilities, and it lags in performance and timeliness. This paper proposes a deep transfer learning-based approach to predict and detect COVID-19 from digital chest radiographs. In this study, three pre-trained convolutional neural network-based models (VGG16, ResNet18, and DenseNet121) have been fine tuned to detect COVID-19 infected patients from chest X-rays (CXRs). The most efficient model is further used to identify the affected regions using an unsupervised gradient-based localization technique. The proposed system uses a classification approach (normal vs. COVID-19 vs. pneumonia vs. lung opacity) using three supervised classification algorithms followed by gradient-based localization. The training, validation and testing of the system are performed using 21165 CXR images (10192 normal, 1345 pneumonia, 3616 COVID-19, and 6012 lung opacity). Simulation and evaluation results are presented using standard performance metrics, viz, accuracy, sensitivity, and specificity.

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Section: Results and Observationsmentioning

confidence: 87%

Section: Related Workmentioning

confidence: 99%

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Prediction and Detection of COVID-19 from Chest X-Rays using Transfer Learning based Deep Convolutional Neural Networks

Misra

Panigrahi

2021

Preprint

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“…Most patients have an air bronchogram 60 . The distribution characteristics of the abnormalities on X‐ray images about these five types of pneumonia are similar to those of CT images (slices) 52,61‐73 . Although the collected 2D data (e.g., X‐ray images) in our proposed data set misses lots of information (original intensity level, spacing, etc.)…”

Section: Proposed Covid‐19 Pneumonia Data Setmentioning

confidence: 75%

Learning to learn by yourself: Unsupervised meta‐learning with self‐knowledge distillation for COVID‐19 diagnosis from pneumonia cases

et al. 2021

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The goal of diagnosing the coronavirus disease 2019 (COVID‐19) from suspected pneumonia cases, that is, recognizing COVID‐19 from chest X‐ray or computed tomography (CT) images, is to improve diagnostic accuracy, leading to faster intervention. The most important and challenging problem here is to design an effective and robust diagnosis model. To this end, there are three challenges to overcome: (1) The lack of training samples limits the success of existing deep‐learning‐based methods. (2) Many public COVID‐19 data sets contain only a few images without fine‐grained labels. (3) Due to the explosive growth of suspected cases, it is urgent and important to diagnose not only COVID‐19 cases but also the cases of other types of pneumonia that are similar to the symptoms of COVID‐19. To address these issues, we propose a novel framework called Unsupervised Meta‐Learning with Self‐Knowledge Distillation to address the problem of differentiating COVID‐19 from pneumonia cases. During training, our model cannot use any true labels and aims to gain the ability of learning to learn by itself. In particular, we first present a deep diagnosis model based on a relation network to capture and memorize the relation among different images. Second, to enhance the performance of our model, we design a self‐knowledge distillation mechanism that distills knowledge within our model itself. Our network is divided into several parts, and the knowledge in the deeper parts is squeezed into the shallow ones. The final results are derived from our model by learning to compare the features of images. Experimental results demonstrate that our approach achieves significantly higher performance than other state‐of‐the‐art methods. Moreover, we construct a new COVID‐19 pneumonia data set based on text mining, consisting of 2696 COVID‐19 images (347 X‐ray + 2349 CT), 10,155 images (9661 X‐ray + 494 CT) about other types of pneumonia, and the fine‐grained labels of all. Our data set considers not only a bacterial infection or viral infection which causes pneumonia but also a viral infection derived from the influenza virus or coronavirus.

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“…Heart disease identification method is also proposed in [31] using novel feature selection and classification algorithms. [32] also uses machine learning classification algorithms for medical datasets. Feature extraction is performed using convolution neural networks and then data is classified into diseased and healthy classes.…”

Section: Literature Reviewmentioning

confidence: 99%

A Knowledge-Based Clinical Decision Support System Utilizing an Intelligent Ensemble Voting Scheme for Improved Cardiovascular Disease Prediction

Bashir¹,

Almazroi

Ashfaq

et al. 2021

IEEE Access

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A massive amount of medical data is available in healthcare industry, which can be utilized to extract useful knowledge. A Clinical Decision Support System (CDSS) is used to improve patient"s safety by minimizing medical errors. Heart disease is one of the major chronic maladies even in todays" world. Many researchers have employed different data mining techniques to predict heart disease. The objective of proposed framework is to improve the accuracy of heart disease prediction. In this paper, an ensemble based voting scheme is proposed to efficiently predict heart disease. Four benchmark heart disease datasets from UCI repository have been utilized for experimentation and evaluation. The performance of the proposed ensemble is compared with individual classifiers as well as with five different ensemble schemes using various parameters in order to show the effectiveness of the proposed ensemble scheme. The evaluation of results shows that the proposed ensemble scheme has better average accuracy (83%) as compared to other ensemble schemes as well as individual classifiers.

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An Uncertainty-Aware Transfer Learning-Based Framework for COVID-19 Diagnosis

Cited by 131 publications

References 21 publications

Prediction and Detection of COVID-19 from Chest X-Rays using Transfer Learning based Deep Convolutional Neural Networks

Prediction and Detection of COVID-19 from Chest X-Rays using Transfer Learning based Deep Convolutional Neural Networks

Learning to learn by yourself: Unsupervised meta‐learning with self‐knowledge distillation for COVID‐19 diagnosis from pneumonia cases

A Knowledge-Based Clinical Decision Support System Utilizing an Intelligent Ensemble Voting Scheme for Improved Cardiovascular Disease Prediction

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