COVID-19: a new deep learning computer-aided model for classification

Elzeki, Omar M.; Shams, Mahmoud Y.; Sarhan, Shahenda; Elfattah, Mohamed Abd; Hassanien, Aboul Ella

doi:10.7717/peerj-cs.358

Cited by 37 publications

(25 citation statements)

References 34 publications

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“…To detect COVID-19, several approaches have been proposed based on deep learning techniques. In Elzeki et al (2021) , the authors used three pre-trained CNN models based on three different chest X-ray sets. Also, in Kamal et al (2021) , the authors used eight pre-trained CNN models to detect COVID-19 from chest X-rays.…”

Section: Related Workmentioning

confidence: 99%

Chest X-ray pneumothorax segmentation using U-Net with EfficientNet and ResNet architectures

Abedalla

Abdullah

Al‐Ayyoub

et al. 2021

PeerJ Computer Science

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Medical imaging refers to visualization techniques to provide valuable information about the internal structures of the human body for clinical applications, diagnosis, treatment, and scientific research. Segmentation is one of the primary methods for analyzing and processing medical images, which helps doctors diagnose accurately by providing detailed information on the body’s required part. However, segmenting medical images faces several challenges, such as requiring trained medical experts and being time-consuming and error-prone. Thus, it appears necessary for an automatic medical image segmentation system. Deep learning algorithms have recently shown outstanding performance for segmentation tasks, especially semantic segmentation networks that provide pixel-level image understanding. By introducing the first fully convolutional network (FCN) for semantic image segmentation, several segmentation networks have been proposed on its basis. One of the state-of-the-art convolutional networks in the medical image field is U-Net. This paper presents a novel end-to-end semantic segmentation model, named Ens4B-UNet, for medical images that ensembles four U-Net architectures with pre-trained backbone networks. Ens4B-UNet utilizes U-Net’s success with several significant improvements by adapting powerful and robust convolutional neural networks (CNNs) as backbones for U-Nets encoders and using the nearest-neighbor up-sampling in the decoders. Ens4B-UNet is designed based on the weighted average ensemble of four encoder-decoder segmentation models. The backbone networks of all ensembled models are pre-trained on the ImageNet dataset to exploit the benefit of transfer learning. For improving our models, we apply several techniques for training and predicting, including stochastic weight averaging (SWA), data augmentation, test-time augmentation (TTA), and different types of optimal thresholds. We evaluate and test our models on the 2019 Pneumothorax Challenge dataset, which contains 12,047 training images with 12,954 masks and 3,205 test images. Our proposed segmentation network achieves a 0.8608 mean Dice similarity coefficient (DSC) on the test set, which is among the top one-percent systems in the Kaggle competition.

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Section: Related Workmentioning

confidence: 99%

Chest X-ray pneumothorax segmentation using U-Net with EfficientNet and ResNet architectures

Abedalla

Abdullah

Al‐Ayyoub

et al. 2021

PeerJ Computer Science

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“…These methods are an appropriate step to employ in all statistical/econometric modeling. Furthermore, there exists a potential use in machine, deep, and statistical learning models [39,[59][60][61]. Future research will consider other variables and relationships, such as production linkages and employment effects of the COVID-19 pandemic phenomena.…”

Section: Conclusion and Future Researchmentioning

confidence: 99%

Modeling COVID-19 Cases Statistically and Evaluating Their Effect on the Economy of Countries

et al. 2021

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COVID-19 infections have plagued the world and led to deaths with a heavy pneumonia manifestation. The main objective of this investigation is to evaluate the performance of certain economies during the crisis derived from the COVID-19 pandemic. The gross domestic product (GDP) and global health security index (GHSI) of the countries belonging–or not–to the Organization for Economic Cooperation and Development (OECD) are considered. In this paper, statistical models are formulated to study this performance. The models’ specifications include, as the response variable, the GDP variation/growth percentage in 2020, and as the covariates: the COVID-19 disease rate from its start in March 2020 until 31 December 2020; the GHSI of 2019; the countries’ risk by default spreads from July 2019 to May 2020; belongingness or not to the OECD; and the GDP per capita in 2020. We test the heteroscedasticity phenomenon present in the modeling. The variable “COVID-19 cases per million inhabitants” is statistically significant, showing its impact on each country’s economy through the GDP variation. Therefore, we report that COVID-19 cases affect domestic economies, but that OECD membership and other risk factors are also relevant.

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“… Elzeki et al (2021) presented a new deep learning computer-aided scheme for rapid and seamless classification of COVID-19. Consisting of three separate COVID-19 X-ray datasets, the study presented the COVID Network (CXRVN) model for assessing grayscale chest X-ray images.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The results of the performed experiments showed that the new multi-model and multi-data approach achieved improved performance over the traditional machine learning models. Elzeki et al (2021) presented a new deep learning computer-aided scheme for rapid and seamless classification of COVID-19. Consisting of three separate COVID-19 X-ray datasets, the study presented the COVID Network (CXRVN) model for assessing grayscale chest X-ray images.…”

Section: Introductionmentioning

confidence: 99%

Modeling a deep transfer learning framework for the classification of COVID-19 radiology dataset

Fayemiwo

Olowookere

Arekete

et al. 2021

PeerJ Computer Science

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Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-Coronavirus-2 or SARS-CoV-2), which came into existence in 2019, is a viral pandemic that caused coronavirus disease 2019 (COVID-19) illnesses and death. Research showed that relentless efforts had been made to improve key performance indicators for detection, isolation, and early treatment. This paper used Deep Transfer Learning Model (DTL) for the classification of a real-life COVID-19 dataset of chest X-ray images in both binary (COVID-19 or Normal) and three-class (COVID-19, Viral-Pneumonia or Normal) classification scenarios. Four experiments were performed where fine-tuned VGG-16 and VGG-19 Convolutional Neural Networks (CNNs) with DTL were trained on both binary and three-class datasets that contain X-ray images. The system was trained with an X-ray image dataset for the detection of COVID-19. The fine-tuned VGG-16 and VGG-19 DTL were modelled by employing a batch size of 10 in 40 epochs, Adam optimizer for weight updates, and categorical cross-entropy loss function. The results showed that the fine-tuned VGG-16 and VGG-19 models produced an accuracy of 99.23% and 98.00%, respectively, in the binary task. In contrast, in the multiclass (three-class) task, the fine-tuned VGG-16 and VGG-19 DTL models produced an accuracy of 93.85% and 92.92%, respectively. Moreover, the fine-tuned VGG-16 and VGG-19 models have MCC of 0.98 and 0.96 respectively in the binary classification, and 0.91 and 0.89 for multiclass classification. These results showed strong positive correlations between the models’ predictions and the true labels. In the two classification tasks (binary and three-class), it was observed that the fine-tuned VGG-16 DTL model had stronger positive correlations in the MCC metric than the fine-tuned VGG-19 DTL model. The VGG-16 DTL model has a Kappa value of 0.98 as against 0.96 for the VGG-19 DTL model in the binary classification task, while in the three-class classification problem, the VGG-16 DTL model has a Kappa value of 0.91 as against 0.89 for the VGG-19 DTL model. This result is in agreement with the trend observed in the MCC metric. Hence, it was discovered that the VGG-16 based DTL model classified COVID-19 better than the VGG-19 based DTL model. Using the best performing fine-tuned VGG-16 DTL model, tests were carried out on 470 unlabeled image dataset, which was not used in the model training and validation processes. The test accuracy obtained for the model was 98%. The proposed models provided accurate diagnostics for both the binary and multiclass classifications, outperforming other existing models in the literature in terms of accuracy, as shown in this work.

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COVID-19: a new deep learning computer-aided model for classification

Cited by 37 publications

References 34 publications

Chest X-ray pneumothorax segmentation using U-Net with EfficientNet and ResNet architectures

Chest X-ray pneumothorax segmentation using U-Net with EfficientNet and ResNet architectures

Modeling COVID-19 Cases Statistically and Evaluating Their Effect on the Economy of Countries

Modeling a deep transfer learning framework for the classification of COVID-19 radiology dataset

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