USING CONVOLUTIONAL NEURAL NETWORKS FOR BREAST IMAGING AND TO MO SYNTHESIS CATEGORIZATION
Abstract
In the United States, 99 out of every 100 people diagnosed with breast cancer are women. About 12 percent of all girls in the United States may be diagnosed with breast cancer sooner or later in their lives. Currently, breast cancer is the type of aggressive cancer that most often affects women. This mortality rate related to breast cancer has been shown to have a preferred declining trend over the past several decades. But, because of the large number of breast cancer diagnoses each year, about 40,000 people in the United States die as an immediate result of the disease. When cancer is detected in the early stages, cancer cells are most likely to be located in a localized part of the body. As a result, it is easier to control the disease when the right medicinal drug is introduced. While cancer cells spread to other areas of the frame, it is far more difficult to deal with and subsequently treat the disorder.
Keywords
convolutional, networks, medicinal drug, cancer diagnosesHow to Cite
References
Siegel, RL, KD Miller, and A. Jemal, Cancer Statistics, 2017. CA Cancer Jeclin, 2017. 67 (1): p. 7-30.
Kim, SY, et al., Screening-detected breast cancer: clinical-pathological and imaging factors associated with survival rates and recurrence. Radiology, 2017. 284 (2): p. 354–364.
Tests on, A., et al., Consequences of false-positive screening mammograms.
Jam Internal Medicine, 2014. 174 (6): pages 954–961.
Puroljal, J, et al., Breast cancer screening (BCS) charts: a basic and preliminary model for making screening mammography more productive and efficient. J public health ( oxf ), 2017: p.1-8.
, DB, Digital breast tomosynthesis: a better mammogram. Radiology, 2013. 267 (3): p. 968-9.
But, Y., Y. Bengio, and G. Hinton, Deep Learning. Nature, 2015. 521 (7553): pages 436–444.
Lekan, Y., et al., Backpropagation implemented for handwritten zip code recognition
Neural Computation, 1989. 1 (4): p.541-551.
Bengio, Y., A. Courville, and P. Vincent, Representation Learning: A Review and New Approach. IEEE Transpattern mal machine tell , 2013. 35 (8): p.1798–828.
Wan, L., et al. Regularization of neural networks using drop connect, In Proceedings of the 30th International Conference on Machine Learning (ICML-13). 2013.
Li, CY, Gallagher PW, and Z Tu, Generalization of Pooling Functions in Convolutional Neural Networks: Mixed. Gated, and Tree. Archive Preprint, 2015. 1509.
Netzer , Y., et al. Reading digits in natural images with unsupervised feature learning. NIPS Workshop on Deep Learning and Unsupervised Feature Learning. 2011.
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Copyright (c) 2020 Lakhendra Kumar
This work is licensed under a Creative Commons Attribution 4.0 International License.
Individual articles are published Open Access under the Creative Commons Licence: CC-BY 4.0.
