Breast cancer classification using deep belief networks

By Ahmed M. Abdel-Zaher, Ayman M. Eldeib. Expert systems with applications, 2016

451 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

A deep learning approach for credit scoring using credit default swaps (2017, background): “…with success in classification task, e.g., computer vision (Krizhevsky et al., 2012), health state classification (Tamilselvan and Wang, 2013; Abdel-Zaher and Eldeib, 2016), speech and language processing (Mohamed et al., 2012; Ling et al., 2013) and emotion recognition (Le and Provost,…”
Deep Learning and Medical Diagnosis: A Review of Literature (2018, unknown): “[25] DBN-NN Mammography Automatic diagnosis for detecting breast cancer; the accuracy of the overall neural network was 99.”
A survey of deep neural network architectures and their applications (2017, methodology): “[1] proposed an automatic diagnosis system which includes a DBN for pretraining and a backpropagation NN for fine-tuning.”
Deep belief network based electricity load forecasting: An analysis of Macedonian case (2016, methodology): “Today, the DBNmodel is usedmostly for image classification [41,42] and as such it is also used in medicine for automatic detection of breast cancer [43] and selecting risk factors and prediction of osteoporosis [44].”