id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-190424-466a35jf	Lee, Sang Won	Darwin's Neural Network: AI-based Strategies for Rapid and Scalable Cell and Coronavirus Screening	2020-07-22		.txt	text/plain	5680	302	51	Here we adapt the theory of survival of the fittest in the field of computer vision and machine perception to introduce a new framework of multi-class instance segmentation deep learning, Darwin's Neural Network (DNN), to carry out morphometric analysis and classification of COVID19 and MERS-CoV collected in vivo and of multiple mammalian cell types in vitro. U-Net with Inception ResNet v2 backbone yielded the highest global accuracy of 0.8346, as seen in Figure 4(E) ; therefore, Inception-ResNet-v2 was integrated in the place of CNN II for DNN for cells. For overall instance segmentation results, DNN produced both superior global accuracy and Jaccard Similarity Coefficient for cells and viruses. As observed in Figure 6 (C1-C2) , the DNN analysis showed statistical significance in area and circularity of the COVID19 in comparison to the MERS virus particles, which aligned with findings in the ground truth data of the viruses.	./cache/cord-190424-466a35jf.txt	./txt/cord-190424-466a35jf.txt