Deep Transfer Learning Based Classification Model for COVID-19 Disease

Pathak, Yadunath; Shukla, Prashant Kumar; Tiwari, Akhilesh; Stalin, S.; Singh, Sarva Daman

doi:10.1016/j.irbm.2020.05.003

Cited by 380 publications

(238 citation statements)

References 24 publications

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“…To compare our results with existence work, Table 2 illustrates this comparsion. ResNet-50 Preprocessed dataset of Ieee8023 and Kaggle 96.23% [19] CoroNet Ieee8023+ Kaggle 87% [28] deep transfer learning technique images are collected from various datasets 93.0189% [27] COVID-Net COVIDx dataset 92.4% [22] Xception and ResNet-50V2 Ieee8023 +Kaggle 91.4% [18] COVIDX-Net Ieee8023 90% [24] CNN RYDLS-20 89% [23] nCOVnet Ieee8023 88%…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Recognition of corona virus disease (COVID-19) using deep learning network

Abdulmunem

Abutiheen

Aleqabie

2021

IJECE

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Corona virus disease (COVID-19) has an incredible influence in the last few months. It causes thousands of deaths in round the world. This make a rapid research movement to deal with this new virus. As a computer science, many technical researches have been done to tackle with it by using image processing algorithms. In this work, we introduce a method based on deep learning networks to classify COVID-19 based on x-ray images. Our results are encouraging to rely on to classify the infected people from the normal. We conduct our experiments on recent dataset, Kaggle dataset of COVID-19 X-ray images and using ResNet50 deep learning network with 5 and 10 folds cross validation. The experiments results show that 5 folds gives effective results than 10 folds with accuracy rate 97.28%.

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Section: Resultsmentioning

confidence: 99%

“…In [28] the COVID-19 contaminated patients were identified using the deep transfer technique. In comparison, a top 2 smooth loss function with cost-sensitive characteristics has been dealt with the noisy and imbalanced datasets COVID-19.…”

Section: Related Workmentioning

confidence: 99%

Recognition of corona virus disease (COVID-19) using deep learning network

Abdulmunem

Abutiheen

Aleqabie

2021

IJECE

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“…The method by the Ouchicha, Ammor [23] and Narayan Das, Kumar [24] achieved 97.2% and 97.4% overall testing accuracy in an unbalanced dataset where only 219 and 127 COVID-19 images were used for model building. At the same time, Pathak, Shukla [25] and Shaban, Rabie [26] used CT images and achieved 93% accuracy applying small COVID-19 images. Some other studies [16,17,[27][28][29][30][31]introduced different approaches for early detection of COVID-19 using X-ray and CT images indicating lower than or around 90% accuracy rate.…”

Section: Resultsmentioning

confidence: 99%

DenseNet Convolutional Neural Networks Application for Predicting COVID-19 Using CT Image

Hasan

Bao

Shawon

2020

Preprint

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Public health and human lives recently have been impacted by the devastating effect of Coronavirus 2019. This catastrophic effect has destroyed the human experience by creating a chaotic healthcare situation infinitely more destructive than the Second World War. Strong communicable characteristics of COVID-19 among human communities make the world’s situation a severe pandemic. Due to the unavailable vaccine of COVID-19 to control rather than cure, early and accurate detection of the virus can be a promising technique for tracking and preventing the infection spreading (e.g., by isolating the patients). This situation indicates to improve auxiliary COVID-19 detection technique. Computed tomography (CT) imaging is a mostly used technique for pneumonia because of its common availability. The application of artificial intelligence systems integrated with images can be a promising alternative for the identification of COVID-19. This paper presents a promising technique of predicting COVID-19 patients from the CT image using convolutional neural networks (CNN). The novel approach is based on DenseNet is the updated CNN architecture in the present state to detect COVID-19. The results outperformed 92% accuracy, with 95% recall showing good performance for the identification of COVID-19.

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“…Transfer learning on pre-trained Xception CNN architecture is utilized by authors of [23] to classify X-ray images into 4 classes, namely COVID-19, Pneumonia bacterial, pneumonia viral, Normal; the proposed model achieved an accuracy of 89.6 % on 4 class classification and 95% on 3 class classification. In [24] , t he authors utilized Resnet for feature extraction upon which a classification model is used to classify an image as COVID and Non-COVID. The cost-sensitive top-2 smooth loss function is used to improve the outcomes further.…”

Section: Related Workmentioning

confidence: 99%

InstaCovNet-19: A deep learning classification model for the detection of COVID-19 patients using Chest X-ray

Gupta

Anjum

Gupta

et al. 2021

Applied Soft Computing

197

121

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Recently, the whole world became infected by the newly discovered coronavirus (COVID-19). SARS-CoV-2, or widely known as COVID-19, has proved to be a hazardous virus severely affecting the health of people. It causes respiratory illness, especially in people who already suffer from other diseases. Limited availability of test kits as well as symptoms similar to other diseases such as pneumonia has made this disease deadly, claiming the lives of millions of people. Artificial intelligence models are found to be very successful in the diagnosis of various diseases in the biomedical field In this paper, an integrated stacked deep convolution network InstaCovNet-19 is proposed. The proposed model makes use of various pre-trained models such as ResNet101, Xception, InceptionV3, MobileNet, and NASNet to compensate for a relatively small amount of training data. The proposed model detects COVID-19 and pneumonia by identifying the abnormalities caused by such diseases in Chest X-ray images of the person infected. The proposed model achieves an accuracy of 99.08% on 3 class (COVID-19, Pneumonia, Normal) classification while achieving an accuracy of 99.53% on 2 class (COVID, NON-COVID) classification. The proposed model achieves an average recall, F1 score, and precision of 99%, 99%, and 99%, respectively on ternary classification, while achieving a 100% precision and a recall of 99% on the binary class., while achieving a 100% precision and a recall of 99% on the COVID class. InstaCovNet-19’s ability to detect COVID-19 without any human intervention at an economical cost with high accuracy can benefit humankind greatly in this age of Quarantine.

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Deep Transfer Learning Based Classification Model for COVID-19 Disease

Cited by 380 publications

References 24 publications

Recognition of corona virus disease (COVID-19) using deep learning network

Recognition of corona virus disease (COVID-19) using deep learning network

DenseNet Convolutional Neural Networks Application for Predicting COVID-19 Using CT Image

InstaCovNet-19: A deep learning classification model for the detection of COVID-19 patients using Chest X-ray

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