Geodesic
Efficient Generative transfer learning framework for the detection of COVID-19
Computer Science and Information Systems, Tome 19 (2022) no. 3.

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Deep learning plays a major role in detecting the presence of Coron-avirus 2019 (COVID-19) and demands huge data. Availability of annotated data is a hurdle in using Deep learning technique. To enhance the accuracy of detection Deep Convolutional Generative Adversarial Network (DCGAN) is used to generate synthetic data. Densenet-201 is identified as the deep learning framework to de-tect COVID-19 from X-ray images. In this research, to validate the effectiveness of the Densenet-201, we explored conventional machine learning approaches such as SVM, Random Forest and Convolutional Neural Network (CNN). The feature map for training the machine learning approaches are extracted using Densenet-201 as feature extractor. The results show that Densenet-201 as feature representation with SVM is performing well in detecting COVID-19 with high accuracy. More-over we experimented the proposed methodology without using DCGAN as well. DenseNet-201 based approach is capable of detecting the presence of COVID-19 with high accuracy. Experiments demonstrated that the proposed transfer learning approach based on DenseNet-201 along with DCGAN based augmentation outper-forms the State of the art approaches like ResNet50, CNN, and VGG-16.
Keywords: COVID-19, Densenet-201, DCGAN, Disease Classification, Data Augmentation, Deep learning 
@article{CSIS_2022_19_3_a12,
     author = {J. Bhuvana and T. T. Mirnalinee and B. Bharathi and Infant Sneha},
     title = {Efficient {Generative} transfer learning framework for the detection of {COVID-19}},
     journal = {Computer Science and Information Systems},
     publisher = {mathdoc},
     volume = {19},
     number = {3},
     year = {2022},
     url = {http://geodesic.mathdoc.fr/item/CSIS_2022_19_3_a12/}
}
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J. Bhuvana; T. T. Mirnalinee; B. Bharathi; Infant Sneha. Efficient Generative transfer learning framework for the detection of COVID-19. Computer Science and Information Systems, Tome 19 (2022) no. 3. http://geodesic.mathdoc.fr/item/CSIS_2022_19_3_a12/