id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-127759-wpqdtdjs	Qi, Xiao	Chest X-ray Image Phase Features for Improved Diagnosis of COVID-19 Using Convolutional Neural Network	2020-11-06		.txt	text/plain	3896	250	50	In this study, we design a novel multi-feature convolutional neural network (CNN) architecture for multi-class improved classification of COVID-19 from CXR images. In this work we show how local phase CXR features based image enhancement improves the accuracy of CNN architectures for COVID-19 diagnosis. Our proposed method is designed for processing CXR images and consists of two main stages as illustrated in Figure 1 : 1-We enhance the CXR images (CXR(x, y)) using local phase-based image processing method in order to obtain a multi-feature CXR image (M F (x, y)), and 2-we classify CXR(x, y) by designing a deep learning approach where multi feature CXR images (M F (x, y)), together with original CXR data (CXR(x, y)), is used for improving the classification performance. Our proposed multi-feature CNN architectures were trained on a large dataset in terms of the number of COVID-19 CXR scans and have achieved improved classification accuracy across all classes.	./cache/cord-127759-wpqdtdjs.txt	./txt/cord-127759-wpqdtdjs.txt