CovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved Covid-19 Detection

Waheed, Abdul; Goyal, Muskan; Gupta, Deepak; Khanna, Ashish; Al‐Turjman, Fadi; Pinheiro, Plácido Rogério

doi:10.1109/access.2020.2994762

Cited by 589 publications

(397 citation statements)

References 30 publications

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“…Unavailability of large number of image data of Covid-19 +ve patients is a challenge faced by most researchers working in this area. Development of CovidGAN for the generation of data artificially has been done in a work [21] -which in turn will help in improved Covid-19 detection.…”

Section: Related Workmentioning

confidence: 99%

Automatic COVID-19 Detection from X-Ray images using Ensemble Learning with Convolutional Neural Network

Das

Ghosh

Thunder

et al. 2020

Preprint

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Covid-19 continues to have catastrophic effects on the lives of human beings throughout the world. To combat this disease it is necessary to screen the affected patients in a fast and inexpensive way. One of the most viable steps towards achieving this goal is through radiological examination, Chest X-Ray being the most easily available and least expensive option. In this paper we have proposed a Deep Convolutional Neural Network based solution which can detect the Covid-19 +ve patients using chest X-Ray images. To test the efficacy of the solution we have used publicly available chest X-ray images of Covid +ve and -ve cases. 538 images of Covid +ve patients and 468 images of Covid -ve patients have been divided into 771 trainable images and 235 testing images. Our solution gave a classification accuracy of 95.7% and sensitivity of 98% in the test set-up. We have developed a GUI application for public use. This application can be used on any computer by any medical personnel to detect Covid +ve patients using Chest X-Ray images within a very few seconds.

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Section: Related Workmentioning

confidence: 99%

Automatic COVID-19 Detection from X-Ray images using Ensemble Learning with Convolutional Neural Network

Das

Ghosh

Thunder

et al. 2020

Preprint

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“…The experimental results showed that the DarkCovidNet model had diagnosed the COVID-19 with higher accuracy by using the two-classes dataset, where the accuracy reached 98.08%. In [16] to improve CNN architectures’ performance in diagnosing COVID-19, a new model called CovidGAN is built, generating new samples from the dataset samples used. The experiment results demonstrated that the CovidGAN model helped the VGG16 network diagnose the COVID-19 with 95% accuracy.…”

Section: Introductionmentioning

confidence: 99%

An optimized deep learning architecture for the diagnosis of COVID-19 disease based on gravitational search optimization

Ezzat

Hassanien

Ella

2021

Applied Soft Computing

138

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In this paper, a novel approach called GSA-DenseNet121-COVID-19 based on a hybrid convolutional neural network (CNN) architecture is proposed using an optimization algorithm. The CNN architecture that was used is called DenseNet121, and the optimization algorithm that was used is called the gravitational search algorithm (GSA). The GSA is used to determine the best values for the hyperparameters of the DenseNet121 architecture. To help this architecture to achieve a high level of accuracy in diagnosing COVID-19 through chest x-ray images. The obtained results showed that the proposed approach could classify 98.38% of the test set correctly. To test the efficacy of the GSA in setting the optimum values for the hyperparameters of DenseNet121. The GSA was compared to another approach called SSD-DenseNet121, which depends on the DenseNet121 and the optimization algorithm called social ski driver (SSD). The comparison results demonstrated the efficacy of the proposed GSA-DenseNet121-COVID-19. As it was able to diagnose COVID-19 better than SSD-DenseNet121 as the second was able to diagnose only 94% of the test set. The proposed approach was also compared to another method based on a CNN architecture called Inception-v3 and manual search to quantify hyperparameter values. The comparison results showed that the GSA-DenseNet121-COVID-19 was able to beat the comparison method, as the second was able to classify only 95% of the test set samples. The proposed GSA-DenseNet121-COVID-19 was also compared with some related work. The comparison results showed that GSA-DenseNet121-COVID-19 is very competitive.

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“…Tomar and Gupta ( 25 ) used Long Short-Term Memory (LSTM) and curve fitting for forecasting the number of COVID-19 confirmed cases in India 30 days ahead. The main limitation is that the proposed method is accurate only for a short range of values ( 26 ).…”

Section: Introductionmentioning

confidence: 99%

COVID-19 Mortality Rate Prediction for India Using Statistical Neural Network Models

Dhamodharavadhani

Rathipriya

Chatterjee³

2020

Front. Public Health

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The primary aim of this study is to investigate suitable Statistical Neural Network (SNN) models and their hybrid version for COVID-19 mortality prediction in Indian populations and is to estimate the future COVID-19 death cases for India. SNN models such as Probabilistic Neural Network (PNN), Radial Basis Function Neural Network (RBFNN), and Generalized Regression Neural Network (GRNN) are applied to develop the COVID-19 Mortality Rate Prediction (MRP) model for India. For this purpose, we have used two datasets as D1 and D2. The performances of these models are evaluated using Root Mean Square Error (RMSE) and “R,” a correlation value between actual and predicted value. To improve prediction accuracy, the new hybrid models have been constructed by combining SNN models and the Non-linear Autoregressive Neural Network (NAR-NN). This is to predict the future error of the SNN models, which adds to the predicted value of these models for getting better MRP value. The results showed that the PNN and RBFNN-based MRP model performed better than the other models for COVID-19 datasets D2 and D1, respectively.

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CovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved Covid-19 Detection

Cited by 589 publications

References 30 publications

Automatic COVID-19 Detection from X-Ray images using Ensemble Learning with Convolutional Neural Network

Automatic COVID-19 Detection from X-Ray images using Ensemble Learning with Convolutional Neural Network

An optimized deep learning architecture for the diagnosis of COVID-19 disease based on gravitational search optimization

COVID-19 Mortality Rate Prediction for India Using Statistical Neural Network Models

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