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بحث عن Artificial Intelligence Techniques for Breast Cancer Battle

عدد الصفحات 21

Contents

Introduction: 3

Symptoms of Breast Cancer: 5

Stages of Breast Cancer: 6

Diagnosis of Breast Cancer: 7

Artificial Intelligence for Breast Cancer: 8

How Artificial intelligence could reveal new breast cancer types?. 11

Impact of AI on Disease Treatment 12

Applying AI to Hormone-Connected Breast Cancers. 12

Identifying Who Might Benefit From Immunotherapy. 13

AI Accurately and Efficiently Improves Breast Cancer Detecting Technology. 14

Artificial Intelligence to Recognise Patterns in Breast Cancer. 16

Artificial Intelligence Method Predicts Future Risk of Breast Cancer. 18

Conclusion: 20

References: 21

 

References:

 

  1. Mario Coccia, (2011), Artificial Intelligence Technology In Oncology: A New Technological Paradigm, National Research Council Of Italy & Yale University, Yale University School Of Medicine, Global Oncology, Yale Comprehensive Cancer Centre.
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  3. The national artificial intelligence research and development strategic plan. In: Council NSaT, editor. United States: networking and Information Technology Research and Development Subcommittee; US: National Science and Technology Council; 2016. p. 1–40.
  4. Houssami N, Lee CI, Buist DSM, et al. Artificial intelligence for breast cancer screening: opportunity or hype? Breast. 2017; 36:31–33.
  5. Trister AD, Buist D, Lee CI. Will machine learning tip the balance in breast cancer screening? JAMA Oncol. 2017;3:1463
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  7. Qiu Y, Yan S, Gundreddy RR, et al. A new approach to develop computer-aided diagnosis scheme of breast mass classification using deep learning technology. J Xray Sci Technol. 2017; 25 (5):751–763. PubMed PMID: 28436410.
  8. Sun W, Tseng T-L, Zhang J, et al. Enhancing deep convolutional neural network scheme for breast cancer diagnosis with unlabeled data. Computerized Med Imaging Graphics. 2017;57:4–9.
  9. Saraswathi D, Srinivasan E. A CAD system to analyse mammogram images using fully complex-valued relaxation neural network ensembled classifier. J Med Eng Technol. 2014 Oct 01;38(7):359–366.
  10. Velikova M, Lucas PJF, Samulski M, et al. On the interplay of machine learning and background knowledge in image interpretation by Bayesian networks. Artif Intell Med. 2013 Jan 01;57(1):73–86​
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