Multi-Channel Transfer Learning of Chest X-ray Images for Screening of COVID-19

Misra, Sampa; Jeon, Seungwan; Lee, Ben Seiyon; Managuli, Ravi; Jang, In Su; Kim, Chulhong

doi:10.3390/electronics9091388

Cited by 84 publications

(80 citation statements)

References 31 publications

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“…By combining three different models which were fine-tuned on 3 datasets, Misra et al. [41] designed a multi-channel ensemble TL method based on ResNet-18 in such a way that the model could extract more relevant features for each class and identify COVID-19 features more accurately from the X-ray images.…”

Section: Introductionmentioning

confidence: 99%

Does non-COVID-19 lung lesion help? investigating transferability in COVID-19 CT image segmentation

Wang

Zhang

Liu

et al. 2021

Computer Methods and Programs in Biomedicine

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Background and Objective : Coronavirus disease 2019 (COVID-19) is a highly contagious virus spreading all around the world. Deep learning has been adopted as an effective technique to aid COVID-19 detection and segmentation from computed tomography (CT) images. The major challenge lies in the inadequate public COVID-19 datasets. Recently, transfer learning has become a widely used technique that leverages the knowledge gained while solving one problem and applying it to a different but related problem. However, it remains unclear whether various non-COVID19 lung lesions could contribute to segmenting COVID-19 infection areas and how to better conduct this transfer procedure. This paper provides a way to understand the transferability of non-COVID19 lung lesions and a better strategy to train a robust deep learning model for COVID-19 infection segmentation. Methods : Based on a publicly available COVID-19 CT dataset and three public non-COVID19 datasets, we evaluate four transfer learning methods using 3D U-Net as a standard encoder-decoder method. i) We introduce the multi-task learning method to get a multi-lesion pre-trained model for COVID-19 infection. ii) We propose and compare four transfer learning strategies with various performance gains and training time costs. Our proposed Hybrid-encoder Learning strategy introduces a Dedicated-encoder and an Adapted-encoder to extract COVID-19 infection features and general lung lesion features, respectively. An attention-based Selective Fusion unit is designed for dynamic feature selection and aggregation. Results : Experiments show that trained with limited data, proposed Hybrid-encoder strategy based on multi-lesion pre-trained model achieves a mean DSC, NSD, Sensitivity, F1-score, Accuracy and MCC of 0.704, 0.735, 0.682, 0.707, 0.994 and 0.716, respectively, with better genetalization and lower over-fitting risks for segmenting COVID-19 infection. Conclusions : The results reveal the benefits of transferring knowledge from non-COVID19 lung lesions, and learning from multiple lung lesion datasets can extract more general features, leading to accurate and robust pre-trained models. We further show the capability of the encoder to learn feature representations of lung lesions, which improves segmentation accuracy and facilitates training convergence. In addition, our proposed Hybrid-encoder learning method incorporates transferred lung lesion features from non-COVID19 datasets effectively and achieves significant improvement. These findings promote new insights into transfer learning for COVID-19 CT image segmentation, which can also be further generalized to other medical tasks.

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Section: Introductionmentioning

confidence: 99%

Does non-COVID-19 lung lesion help? investigating transferability in COVID-19 CT image segmentation

Wang

Zhang

Liu

et al. 2021

Computer Methods and Programs in Biomedicine

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“…In this section, a comprehensive comparison of the proposed method is made with existing state-of-the-art deep feature-based CAD methods related to COVID19 diagnostics [13] , [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] , [24] , [25] . Most of these methods used the existing pretrained networks and applied the end-to-end transfer learning approach for the automated diagnosis of COVID19 infection.…”

Section: Results and Analysismentioning

confidence: 99%

“…However, these comparative studies used limited radiographic datasets and different experimental protocols. For a fair comparison, the quantitative results of these baseline methods [13] , [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] , [24] , [25] were assessed based on the selected datasets and experimental protocol. In details, the pretrained backbones of the baseline methods [13] , [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] , [24] , [25] were selected and fine-tuned with the selected datasets.…”

Section: Results and Analysismentioning

confidence: 99%

“…Recent advancements in artificial intelligence (AI) have shown a remarkable breakthrough in the development of computer-aided diagnosis (CAD) tools [6] , [7] , [8] , [9] , [10] , [11] , [12] , [13] , [14] , [15] , [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] , [24] , [25] , [26] , [27] . In particular, with the advent of deep learning algorithms, such automated tools have shown significant decision-making performance in different medical domains, including radiology.…”

Section: Introductionmentioning

confidence: 99%

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Light-weighted ensemble network with multilevel activation visualization for robust diagnosis of COVID19 pneumonia from large-scale chest radiographic database

Owais

Yoon

Mahmood

et al. 2021

Applied Soft Computing

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Currently, the coronavirus disease 2019 (COVID19) pandemic has killed more than one million people worldwide. In the present outbreak, radiological imaging modalities such as computed tomography (CT) and X-rays are being used to diagnose this disease, particularly in the early stage. However, the assessment of radiographic images includes a subjective evaluation that is time-consuming and requires substantial clinical skills. Nevertheless, the recent evolution in artificial intelligence (AI) has further strengthened the ability of computer-aided diagnosis tools and supported medical professionals in making effective diagnostic decisions. Therefore, in this study, the strength of various AI algorithms was analyzed to diagnose COVID19 infection from large-scale radiographic datasets. Based on this analysis, a light-weighted deep network is proposed, which is the first ensemble design (based on MobileNet, ShuffleNet, and FCNet) in medical domain (particularly for COVID19 diagnosis) that encompasses the reduced number of trainable parameters (a total of 3.16 million parameters) and outperforms the various existing models. Moreover, the addition of a multilevel activation visualization layer in the proposed network further visualizes the lesion patterns as multilevel class activation maps (ML-CAMs) along with the diagnostic result (either COVID19 positive or negative). Such additional output as ML-CAMs provides a visual insight of the computer decision and may assist radiologists in validating it, particularly in uncertain situations Additionally, a novel hierarchical training procedure was adopted to perform the training of the proposed network. It proceeds the network training by the adaptive number of epochs based on the validation dataset rather than using the fixed number of epochs. The quantitative results show the better performance of the proposed training method over the conventional end-to-end training procedure. A large collection of CT-scan and X-ray datasets (based on six publicly available datasets) was used to evaluate the performance of the proposed model and other baseline methods. The experimental results of the proposed network exhibit a promising performance in terms of diagnostic decision. An average F1 score (F1) of 94.60% and 95.94% and area under the curve (AUC) of 97.50% and 97.99% are achieved for the CT-scan and X-ray datasets, respectively. Finally, the detailed comparative analysis reveals that the proposed model outperforms the various state-of-the-art methods in terms of both quantitative and computational performance.

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“…The model gave high accuracy of 95.12% with comprehensive dataset of images. Misra et al [ 35 ] presented a multi-channel transfer learning model based on ResNet architecture on different sets of dataset composition. Three classes as normal, pneumonia, and COVID were used as the target values.…”

Section: Related Workmentioning

confidence: 99%

Triage of potential COVID-19 patients from chest X-ray images using hierarchical convolutional networks

Dev

Khowaja

Bist³

et al. 2021

Neural Comput & Applic

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The current COVID-19 pandemic has motivated the researchers to use artificial intelligence techniques for a potential alternative to reverse transcription-polymerase chain reaction due to the limited scale of testing. The chest X-ray (CXR) is one of the alternatives to achieve fast diagnosis, but the unavailability of large-scale annotated data makes the clinical implementation of machine learning-based COVID detection difficult. Another issue is the usage of ImageNet pre-trained networks which does not extract reliable feature representations from medical images. In this paper, we propose the use of hierarchical convolutional network (HCN) architecture to naturally augment the data along with diversified features. The HCN uses the first convolution layer from COVIDNet followed by the convolutional layers from well-known pre-trained networks to extract the features. The use of the convolution layer from COVIDNet ensures the extraction of representations relevant to the CXR modality. We also propose the use of ECOC for encoding multiclass problems to binary classification for improving the recognition performance. Experimental results show that HCN architecture is capable of achieving better results in comparison with the existing studies. The proposed method can accurately triage potential COVID-19 patients through CXR images for sharing the testing load and increasing the testing capacity.

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Multi-Channel Transfer Learning of Chest X-ray Images for Screening of COVID-19

Cited by 84 publications

References 31 publications

Does non-COVID-19 lung lesion help? investigating transferability in COVID-19 CT image segmentation

Does non-COVID-19 lung lesion help? investigating transferability in COVID-19 CT image segmentation

Light-weighted ensemble network with multilevel activation visualization for robust diagnosis of COVID19 pneumonia from large-scale chest radiographic database

Triage of potential COVID-19 patients from chest X-ray images using hierarchical convolutional networks

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