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Aggarwal, Aastha
- Comparative Analysis of Different Deep Learning Models for COVID-19 Detection Using X-Ray Images
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Authors
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1 Research Scholar, NITTTR Chandigarh, 160019, IN
2 Professor & Head, IMEE, NITTTR Chandigarh, 160019, IN
3 Associate Professor, NITTTR Chandigarh, 160019, IN
1 Research Scholar, NITTTR Chandigarh, 160019, IN
2 Professor & Head, IMEE, NITTTR Chandigarh, 160019, IN
3 Associate Professor, NITTTR Chandigarh, 160019, IN
Source
Research Cell: An International Journal of Engineering Sciences, Vol 35, No SP (2023), Pagination: 192-211Abstract
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.Full Text
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