id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-238782-z9nb8cwt	Rajinikanth, Venkatesan	Firefly-Algorithm Supported Scheme to Detect COVID-19 Lesion in Lung CT Scan Images using Shannon Entropy and Markov-Random-Field	2020-04-14		.txt	text/plain	3769	211	50	The proposed work aims to suggest an automated image processing scheme to extract the COVID-19 lesion from the lung CT scan images (CTI) recorded from the patients. This work implements Firefly Algorithm and Shannon Entropy (FA+SE) based multi-threshold to enhance the pneumonia lesion and implements Markov-Random-Field (MRF) segmentation to extract the lesions with better accuracy. The proposed work helped to attain a mean accuracy of>92% during COVID-19 lesion segmentation and in future, it can be used to examine the real clinical lung CTI of COVID-19 patients. The proposed research executes a sequence of techniques, such as artifact removal, Firefly Algorithm and Shannon-Entropy (FA+SE) based multi-thresholding, Markov-Random-Field (MRF) segmentation and validation of the proposed system using a comparison with respect to the Ground-Truth-Image (GTI). Finally, the extracted COVID-19 lesion is compared against the Ground-Truth-Image (GTI) and based on the attained performance values; the superiority of the proposed image processing tool is confirmed.	./cache/cord-238782-z9nb8cwt.txt	./txt/cord-238782-z9nb8cwt.txt