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Advanced Machine Learning Approaches in Cancer Prognosis: Challenges and Applications 2021 ed. [Kõva köide]

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  • Formaat: Hardback, 454 pages, kõrgus x laius: 235x155 mm, kaal: 875 g, 168 Illustrations, color; 68 Illustrations, black and white; XX, 454 p. 236 illus., 168 illus. in color., 1 Hardback
  • Sari: Intelligent Systems Reference Library 204
  • Ilmumisaeg: 30-May-2021
  • Kirjastus: Springer Nature Switzerland AG
  • ISBN-10: 303071974X
  • ISBN-13: 9783030719746
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  • Kõva köide
  • Hind: 141,35 €*
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  • Tavahind: 166,29 €
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  • Formaat: Hardback, 454 pages, kõrgus x laius: 235x155 mm, kaal: 875 g, 168 Illustrations, color; 68 Illustrations, black and white; XX, 454 p. 236 illus., 168 illus. in color., 1 Hardback
  • Sari: Intelligent Systems Reference Library 204
  • Ilmumisaeg: 30-May-2021
  • Kirjastus: Springer Nature Switzerland AG
  • ISBN-10: 303071974X
  • ISBN-13: 9783030719746
Teised raamatud teemal:

This book introduces a variety of advanced machine learning approaches covering the areas of neural networks, fuzzy logic, and hybrid intelligent systems for the determination and diagnosis of cancer. Moreover, the tactical solutions of machine learning have proved its vast range of significance and, provided novel solutions in the medical field for the diagnosis of disease. This book also explores the distinct deep learning approaches that are capable of yielding more accurate outcomes for the diagnosis of cancer. In addition to providing an overview of the emerging machine and deep learning approaches, it also enlightens an insight on how to evaluate the efficiency and appropriateness of such techniques and analysis of cancer data used in the cancer diagnosis. Therefore, this book focuses on the recent advancements in the machine learning and deep learning approaches used in the diagnosis of different types of cancer along with their research challenges and future directions for the targeted audience including scientists, experts, Ph.D. students, postdocs, and anyone interested in the subjects discussed.  


Advances in Machine Learning Approaches in Cancer Prognosis.- Data
Analysis on Cancer Disease using Machine Learning Techniques.- Learning from
multiple modalities of imaging data for cancer detection/diagnosis .- Neural
Network for Lung Cancer diagnosis.- Improved Thyroid Disease Prediction Model
Using Data Mining Techniques with Outlier Detection.- Automated Breast Cancer
Diagnosis Based on Neural Network Algorithms.