id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-035098-zmv0ngf0	Li, Daqiu	Stacked-autoencoder-based model for COVID-19 diagnosis on CT images	2020-11-09		.txt	text/plain	5807	358	57	& A new stacked-autoencoder-based model was proposed for COVID-19 diagnosis that can overcome the gradient disappearance and overfitting caused by deep neural network training on a small dataset to some extent. Firstly, an autoencoder is trained to obtain the input firstorder feature h1 of the original CT scan image data, as shown in Fig. 2 . Similarly, we train the convolutional network detection model on the original partitioned data sets and obtain the test results, as shown in the second row of Table 4 . From the last raw feature maps of Fig. 8 , we can see that our model can extract sample features useful for binary classification from the original CT input image after four-layer autoencoder training alone. Besides, with the release of more and more COVID-19 chest CT scan image datasets, the detection accuracy of such deep learning models as the stacked autoencoder detector will be greatly improved.	./cache/cord-035098-zmv0ngf0.txt	./txt/cord-035098-zmv0ngf0.txt