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Variable Hidden Neuron Ensemble for Mass Classification in Digital Mammograms [Application Notes]

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2 Author(s)
Peter Mc Leod ; Central Queensland Univ., Rockhampton, QLD, Australia ; Brijesh Verma

Digital Mammograms are the gold standard for the early detection and diagnosis of breast cancer. Breast cancer is one of the main causes of cancer deaths in women worldwide. One in nine women in Australia will be diagnosed with breast cancer in their lifetime. Women over 50 years of age in particular are encouraged to have screening using digital mammograms so that cancer can be detected at its early stages. Radiologists are able to examine the images by zooming in, changing contrast and brightness and flag any suspicious areas that require further checkup, however in some cases radiologists are unable to spot tumors. Another challenge for radiologists is to classify the tumors once spotted as a benign or malignant diagnosis. This challenge has brought together computer vision and computational intelligence researchers in order to develop new intelligent techniques that can help radiologists.

Published in:

IEEE Computational Intelligence Magazine  (Volume:8 ,  Issue: 1 )