Advanced Machine Learning Approaches in Cancer Prognosis : Challenges and Applications /

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...

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Bibliographic Details
Corporate Author: SpringerLink (Online service)
Other Authors: Nayak, Janmenjoy (Editor), Favorskaya, Margarita N. (Editor), Jain, Seema (Editor), Naik, Bighnaraj (Editor), Mishra, Manohar (Editor)
Format: eBook
Language:English
Published: Cham : Springer International Publishing : Imprint: Springer, 2021.
Edition:1st ed. 2021.
Series:Intelligent Systems Reference Library, 204
Subjects:
Online Access:Connect to the full text of this electronic book
Description
Summary: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. .
Physical Description:1 online resource (XX, 454 pages 236 illustrations, 168 illustrations in color.)
ISBN:9783030719753
ISSN:1868-4408 ;
DOI:10.1007/978-3-030-71975-3