@inbook {27859,
	title = {Transfer learning-based convolutional neural network for COVID-19 detection with X-ray images},
	booktitle = {Data Science for COVID-19},
	year = {2021},
	pages = {451-466},
	publisher = {Academic Press},
	organization = {Academic Press},
	abstract = {Countries the world over have focused on protecting human health and combatting the COVID-19 outbreak. It has had a destructive effect on human health and daily life. Many people have been infected and have died. It is critical to control and prevent the spread of COVID-19 disease by applying quick alternative diagnostic techniques. Although laboratory tests have been widely applied as diagnostic tools, findings suggest that the application of X-ray and computed tomography images and pretrained deep convolutional neural network (CNN) models can help in the accurate detection of this disease. In this study, we propose a model for COVID-19 diagnosis, applying a deep CNN technique based on raw chest X-ray images of COVID-19 patients, which can be accessed publicly on GitHub. Fifty positive and 50 negative COVID-19 X-ray images for training and 20 positive and 20 negative images for testing phases are included. Because the classification of X-ray images needs a deep architecture to cope with the complicated structure of images, we apply five different architectures of well-known pretrained deep CNN models: VGG16, VGG19, ResNet, DenseNet, and InceptionV3. The pretrained VGG16 model can detect COVID-19 from non-COVID-19 cases with the highest classification performance of 80\% accuracy among the other four proposed models, and it can be used as a helpful tool in the department of radiology. In the proposed model, a limited dataset of COVID-19 X-ray images is used that can provide more accurate performance when the number of instances in the dataset increases.},
	keywords = {Chest X-ray images, Coronavirus (COVID-19), deep learning, Pretrained CNN model, Transfer Learning},
	isbn = {978-0-12-824536-1},
	doi = {https://doi.org/10.1016/B978-0-12-824536-1.00003-4},
	url = {https://www.sciencedirect.com/science/article/pii/B9780128245361000034},
	author = {Sahinbas, Kevser and Ferhat Ozgur {Catak}},
	editor = {Kose, Utku and Gupta, Deepak and Victor Hugo C. {de Albuquerque} and Khanna, Ashish}
}
