id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-269270-i2odcsx7	Sahlol, Ahmed T.	COVID-19 image classification using deep features and fractional-order marine predators algorithm	2020-09-21		.txt	text/plain	7058	437	53	In this paper, we propose an improved hybrid classification approach for COVID-19 images by combining the strengths of CNNs (using a powerful architecture called Inception) to extract features and a swarm-based feature selection algorithm (Marine Predators Algorithm) to select the most relevant features. The proposed COVID-19 X-ray classification approach starts by applying a CNN (especially, a powerful architecture called Inception which pre-trained on Imagnet dataset) to extract the discriminant features from raw images (with no pre-processing or segmentation) from the dataset that contains positive and negative COVID-19 images. 1. Propose an efficient hybrid classification approach for COVID-19 using a combination of CNN and an improved swarm-based feature selection algorithm. 4. Evaluate the proposed approach by performing extensive comparisons to several state-of-art feature selection algorithms, most recent CNN architectures and most recent relevant works and existing classification methods of COVID-19 images.	./cache/cord-269270-i2odcsx7.txt	./txt/cord-269270-i2odcsx7.txt