id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_vybda6uyqnfrtaswahcnifxzzq	Ahmed M. Abdel-Zaher	Breast cancer classification using deep belief networks	2016	6	.pdf	application/pdf	4153	686	49	breast cancer has been developed using deep belief network unsupervised path followed by back propagation In this study, back propagation neural netork initialized by weights from a trained deep belief network with Dheeba, Singh, and Selvi (2014), investigated a new classification approach for detection of breast abnormalities in digital mammograms using Particle Swarm Optimized Wavelet Neural Network In our study, we applied deep belief network (DBN) in an unsupervised phase to learn input features statistics of the original WBCD To increase classifier performance for both architectures, we test conjugate gradient back propagation and Levenberg–Marquardt in neural network learning phase. he experiment conducted by Pauline and Santhakumaran indiating Levenberg–Marquardt learning algorithm gives better clasifier accuracy when used with back-propagation neural network for detecting breast cancer based on DBN unsupervised pre-training Breast cancer classification using deep belief networks Breast cancer classification using deep belief networks Breast cancer classification using deep belief networks	./cache/work_vybda6uyqnfrtaswahcnifxzzq.pdf	./txt/work_vybda6uyqnfrtaswahcnifxzzq.txt