Advanced Machine Learning Approaches in Cancer Prognosis: Challenges and Applications

Nayak, Janmenjoy (Edited by)/ Favorskaya, Margarita N. (Edited by)/ Jain, Seema (Edited by)/ Naik, Bighnaraj (Edited by)/ Mishra, Manohar (Edited by)

ISBN 10: 303071974X / ISBN 13: 9783030719746

Verlag: Springer, 2021

Beschreibung

474 pages. 9.25x6.10x1.26 inches. In Stock. Bestandsnummer des Verkäufers x-303071974X

Bibliografische Details

Titel: Advanced Machine Learning Approaches in ...

Verlag: Springer

Erscheinungsdatum: 2021

Einband: Hardcover

Zustand: Brand New

Über diesen Titel

Inhaltsangabe:

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.  

Von der hinteren Coverseite:

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.