id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-247059-uez654q2	Alom, Md Zahangir	COVID_MTNet: COVID-19 Detection with Multi-Task Deep Learning Approaches	2020-04-07		.txt	text/plain	4709	264	56	We employ our Inception Residual Recurrent Convolutional Neural Network with Transfer Learning (TL) approach for COVID-19 detection and our NABLA-N network model for segmenting the regions infected by COVID-19. For chest X-ray image analysis, due to the scarcity of publicly available COVID-19 X-ray samples, we have trained our model with a pneumonia dataset, and then utilized a Transfer Learning (TL) method for retraining with samples of COVID-19. • The proposed methods are evaluated for both Xray and CT images and achieved promising results for COVID-19 detection and infected region localization tasks. The qualitative results clearly demonstrate that the proposed model is able to segment and detect contaminated regions of COVID-19 accurately from the chest X-ray images. The quantitative and qualitative results clearly show that the proposed classification and segmentation for X-ray images demonstrate promising performance in detection and infected region extraction.	./cache/cord-247059-uez654q2.txt	./txt/cord-247059-uez654q2.txt