Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet

Panwar, Harsh; Gupta, Praveen; Siddiqui, Mohammad Khubeb; Morales-Menéndez, Rubén; Singh, Vaishnavi

doi:10.1016/j.chaos.2020.109944

Cited by 532 publications

(412 citation statements)

References 26 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%

“…This network achieved for detecting COVID-19 cases is 99.50%, and the overall average accuracy for all classes is 91.4%. In [23], proposed a deep learning neural network-based method nCOVnet that considered a fast screening method to detect the COVID-19 by analyzing the X-rays of patients. The experimental result for 318 COVID-19 patients with 97.97%, and 320 COVID-19 negative patients with 98.68%.…”

Section: Related Workmentioning

confidence: 99%

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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%

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 work by Apostolopoulos and Mpesiana [29] has used a transfer learning approach on VGG-19 architecture to classify chest X-ray images into one of the three classes: COVID-19, other types of viral pneumonia, and normal. Panwar et al [30] have also modified a variant of the VGG network to reduce the number of parameters by altering the top layers. Global average pooling is used to down-sample the feature maps before being passed to a two-layer dense classification network.…”

Section: Covid-19 Classification Using Deep Learning Modelsmentioning

confidence: 99%

COVID-19 Screening Using a Lightweight Convolutional Neural Network with Generative Adversarial Network Data Augmentation

2020

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COVID-19 is a disease that can be spread easily with minimal physical contact. Currently, the World Health Organization (WHO) has endorsed the reverse transcription-polymerase chain reaction swab test as a diagnostic tool to confirm COVID-19 cases. This test requires at least a day for the results to come out depending on the available facilities. Many countries have adopted a targeted approach in screening potential patients due to the cost. However, there is a need for a fast and accurate screening test to complement this targeted approach, so that the potential virus carriers can be quarantined as early as possible. The X-ray is a good screening modality; it is quick at capturing, cheap, and widely available, even in third world countries. Therefore, a deep learning approach has been proposed to automate the screening process by introducing LightCovidNet, a lightweight deep learning model that is suitable for the mobile platform. It is important to have a lightweight model so that it can be used all over the world even on a standard mobile phone. The model has been trained with additional synthetic data that were generated from the conditional deep convolutional generative adversarial network. LightCovidNet consists of three components, which are entry, middle, and exit flows. The middle flow comprises five units of feed-forward convolutional neural networks that are built using separable convolution operators. The exit flow is designed to improve the multi-scale capability of the network through a simplified spatial pyramid pooling module. It is a symmetrical architecture with three parallel pooling branches that enable the network to learn multi-scale features, which is suitable for cases wherein the X-ray images were captured from all over the world independently. Besides, the usage of separable convolution has managed to reduce the memory usage without affecting the classification accuracy. The proposed method managed to get the best mean accuracy of 0.9697 with a low memory requirement of just 841,771 parameters. Moreover, the symmetrical spatial pyramid pooling module is the most crucial component; the absence of this module will reduce the screening accuracy to just 0.9237. Hence, the developed model is suitable to be implemented for mass COVID-19 screening.

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“…Artificial intelligence and machine learning approach was equally used to model and forecast cases of COVID-19. The works of [8][9][10][11][12][13][14][15] shows the application of such.…”

Section: The Sir and Seir Modelmentioning

confidence: 99%

Forecasting of New Cases of COVID-19 in Nigeria Using Autoregressive Fractionally Integrated Moving Average Models

Adesina

Onanaye

Okewole

et al. 2020

ARJOM

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The emergence of global pandemic known as COVID-19 has impacted significantly on human lives and measures have been taken by government all over the world to minimize the rate of spread of the virus, one of which is by enforcing lockdown. In this study, Autoregressive fractionally integrated moving average (ARFIMA) Models was used to model and forecast what the daily new cases of COVID-19 would have been ten days after the lockdown was eased in Nigeria and compare to the actual new cases for the period when the lockdown was eased. The proposed model ARFIMA model was compared with ARIMA (1, 0, 0), and ARIMA (1, 0, 1) and found to outperform the classical ARIMA models based on AIC and BIC values. The results show that the rate of spread of COVID-19 would have been significantly less if the strict lockdown had continued. ARFIMA model was further used to model what new cases of COVID-19 would be ten days ahead starting from 31st of August 2020. Therefore, this study recommends that government should further enforce measures to reduce the spread of the virus if business must continue as usual.

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Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet

Cited by 532 publications

References 26 publications

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

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

COVID-19 Screening Using a Lightweight Convolutional Neural Network with Generative Adversarial Network Data Augmentation

Forecasting of New Cases of COVID-19 in Nigeria Using Autoregressive Fractionally Integrated Moving Average Models

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