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Comparative Analysis of Different Deep Learning Models for COVID-19 Detection Using X-Ray Images


Affiliations
1 Research Scholar, NITTTR Chandigarh, 160019, India
2 Professor & Head, IMEE, NITTTR Chandigarh, 160019, India
3 Associate Professor, NITTTR Chandigarh, 160019, India
 

Coronavirus disease (COVID-19), one of the most contagious diseases of the twenty-first century was firstly discovered in Wuhan, China, in December 2019 and has spread aggressively around the globe. It is becoming difficult for the medical staff to quickly detect the ailment and prevent it from spreading due to its rapid spread and rising numbers. As a result, automating the diagnostic process has become necessary. One of the emerging study fields that can more accurately address this issue is medical image analysis and a chest X-ray scan is an efficient screening method for identifying COVID-19 cases by training deep learning models. This paper proposes a neural network for classifying COVID-19 cases from other cases (normal, pneumonia, and lung opacity) that has been pre-trained on a labeled chest X-ray dataset and compares the results with other CNN models (VGG16, VGG19, NASNetLarge, MobileNetV2, InceptionResNetV2, DenseNet121, ResNet50). The models were tested against a publicly accessible chest X-ray dataset in which data augmentation and under-sampling techniques were used to eliminate the problems of data scarcity and data imbalance and found that the suggested model performed best, with an accuracy of 93.70%.

Keywords

COVID-19, Chest X-Ray, Medical Imaging, Deep Learning, Classification, Convolutional Neural Network, Transfer Learning.
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  • Comparative Analysis of Different Deep Learning Models for COVID-19 Detection Using X-Ray Images

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Authors

Aastha Aggarwal
Research Scholar, NITTTR Chandigarh, 160019, India
Maitreyee Dutta
Professor & Head, IMEE, NITTTR Chandigarh, 160019, India
Amit Doegar
Associate Professor, NITTTR Chandigarh, 160019, India

Abstract


Coronavirus disease (COVID-19), one of the most contagious diseases of the twenty-first century was firstly discovered in Wuhan, China, in December 2019 and has spread aggressively around the globe. It is becoming difficult for the medical staff to quickly detect the ailment and prevent it from spreading due to its rapid spread and rising numbers. As a result, automating the diagnostic process has become necessary. One of the emerging study fields that can more accurately address this issue is medical image analysis and a chest X-ray scan is an efficient screening method for identifying COVID-19 cases by training deep learning models. This paper proposes a neural network for classifying COVID-19 cases from other cases (normal, pneumonia, and lung opacity) that has been pre-trained on a labeled chest X-ray dataset and compares the results with other CNN models (VGG16, VGG19, NASNetLarge, MobileNetV2, InceptionResNetV2, DenseNet121, ResNet50). The models were tested against a publicly accessible chest X-ray dataset in which data augmentation and under-sampling techniques were used to eliminate the problems of data scarcity and data imbalance and found that the suggested model performed best, with an accuracy of 93.70%.

Keywords


COVID-19, Chest X-Ray, Medical Imaging, Deep Learning, Classification, Convolutional Neural Network, Transfer Learning.

References