id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-002901-u4ybz8ds	Yu, Chanki	Acral melanoma detection using a convolutional neural network for dermoscopy images	2018-03-07		.txt	text/plain	3513	180	52	We applied a convolutional neural network to dermoscopy images of acral melanoma and benign nevi on the hands and feet and evaluated its usefulness for the early diagnosis of these conditions. To perform the 2-fold cross validation, we split them into two mutually exclusive subsets: half of the total image dataset was selected for training and the rest for testing, and we calculated the accuracy of diagnosis comparing it with the dermatologist's and non-expert's evaluation. CONCLUSION: Although further data analysis is necessary to improve their accuracy, convolutional neural networks would be helpful to detect acral melanoma from dermoscopy images of the hands and feet. In the result of group B by the training of group A images, CNN also showed a higher diagnostic accuracy (80.23%) than that of the non-expert (62.71%) but was similar to that of the expert (81.64%).	./cache/cord-002901-u4ybz8ds.txt	./txt/cord-002901-u4ybz8ds.txt